Navigation method and device for lane-level local map and electronic device

Abstract

The invention discloses a navigation method and device for a lane-level local map and an electronic device. The method comprises the following steps: in response to a received local map navigation starting signal, starting a local map navigation mode; acquiring standard precise map data and perception data; constructing a lane-level local road map based on the standard precise map data and the perception data; rendering a lane-level navigation guide line segment in the lane-level local road map based on the standard precise map data, the navigation route information and the current lane information of the vehicle; and navigating the vehicle based on the lane-level local road map and the lane-level navigation guiding line segment. According to the invention, the technical problems that the high-precision map acquisition and updating cost is high and the standard-precision map cannot complete lane-level guidance in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle navigation technology, and in particular to a navigation method, device and electronic device for lane-level local maps. Background Art

[0002] Providing lane-level guidance on the in-vehicle large-screen system can provide users with clearer navigation instructions, avoiding wrong lanes or the wrong road. Some map manufacturers have already implemented this function in mobile navigation and in-vehicle navigation software. However, map manufacturers often rely on high-precision city maps and path planning algorithms. However, high-precision maps have many significant shortcomings, such as high acquisition and update costs, limited coverage, and the need for a high update frequency to ensure real-time performance. Existing standard map navigation can only provide information such as turning points on the navigation route, distance to turning points, and intersection lane panels, and cannot provide lane-level guidance.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] Embodiments of the present invention provide a lane-level local map navigation method, apparatus, and electronic device to at least address the technical issues in related technologies such as high costs for collecting and updating high-precision maps and the inability of standard-precision maps to provide lane-level guidance.

[0005] According to one aspect of an embodiment of the present invention, a lane-level local map navigation method is provided, comprising: in response to receiving a local map navigation start signal, starting a local map navigation mode; obtaining precise map data and perception data, wherein the precise map data is used to provide road network structure data of roads around a vehicle, and the perception data is used to describe the road environment of the roads around the vehicle; constructing a lane-level local road map based on the precise map data and the perception data, wherein the lane-level local road map is used to display roads within a first preset distance range in front of the vehicle that matches the navigation route, and the lane-level local road map includes lane lines; rendering lane-level navigation guide line segments in the lane-level local road map based on the precise map data, navigation route information, and information about the current lane in which the vehicle is located, wherein the lane-level navigation guide line segments are located within a first preset distance range in front of the vehicle in the navigation interface, information about the current lane in which the vehicle is located is information about the lane in which the vehicle is currently located in the lane-level local road map, and the lane-level navigation guide line segments are used to provide navigation information of the vehicle in the lane-level local road map; and navigating the vehicle based on the lane-level local road map and the lane-level navigation guide line segments.

[0006] Furthermore, the lane-level local road map is also used to display roads within a second preset distance range behind the vehicle, and the area of ​​the second preset distance range is smaller than the area of ​​the first preset distance range.

[0007] Furthermore, the starting point of the lane-level navigation guidance line segment is located at the front position of the vehicle in the navigation interface, and the end point of the lane-level navigation guidance line segment is located on the road within a first preset distance range in front of the vehicle that matches the navigation route in the navigation interface, and the display area of ​​the lane-level local road map is smaller than the display area of ​​the vehicle navigation interface.

[0008] Furthermore, constructing a lane-level local road map based on the precise map data and the perception data includes: determining multiple grids in the ego-vehicle coordinate system based on the precise map data; determining the current frame lane linear point corresponding to the current frame based on the perception data; mapping the current frame lane linear point to the multiple grids in the ego-vehicle coordinate system to obtain a mapping relationship between the current frame lane linear point and the multiple grids, wherein the mapping relationship is used to record the grid to which any current frame lane linear point belongs; in any grid, combining the current frame lane linear points in any grid according to the mapping relationship to obtain the current frame segmentation line in any grid; and constructing a lane-level local road map based on the current frame segmentation line.

[0009] Furthermore, determining multiple grids in the own-vehicle coordinate system based on the precise map data includes: determining a grid division range of the own-vehicle coordinate system based on the precise map data, wherein the grid division range is from a second preset distance behind the vehicle's driving direction to a first fork intersection or a third preset distance in front of the vehicle's driving direction; determining a position to be divided based on a preset division interval and a road section dividing point in the grid division range; dividing the own-vehicle coordinate system based on the position to be divided to obtain multiple grids in the own-vehicle coordinate system.

[0010] Furthermore, determining the position to be divided based on the preset division interval and the road section dividing point in the grid division range includes: in response to the existence of a road section dividing point in the grid division range, determining a first division position based on the preset division interval within a first range, wherein the first range is from the second preset distance behind the vehicle's driving direction to the road section dividing point; determining a second division position based on the preset division interval within a second range, wherein the second range is from the road section dividing point to the first fork intersection or the third preset distance in front of the vehicle's driving direction; determining the position to be divided based on the first division position and the second division position.

[0011] Furthermore, determining the position to be divided based on the preset division interval and the road section dividing points in the grid division range includes: in response to the presence of multiple road section dividing points in the grid division range, determining a third division position based on the preset division interval within a third range, wherein the multiple road section dividing points include a first road section dividing point and a second road section dividing point, the distance between the first road section dividing point and the vehicle is closer than the distance between the second road section dividing point and the vehicle, and the third range is from the second preset distance behind the vehicle's driving direction to the first road section dividing point; determining a fourth division position based on the preset division interval within a fourth range, wherein the fourth range is from the first road section dividing point to the second road section dividing point; determining a fifth division position based on the preset division interval within a fifth range, wherein the fifth range is from the second road section dividing point to the first fork in front of the vehicle or the third preset distance; determining the position to be divided based on the third division position, the fourth division position and the fifth division position.

[0012] Furthermore, in any grid, the current frame lane line type points in any grid are combined according to the mapping relationship to obtain the current frame segmentation line in any grid, including: determining the lane lines in the road around the vehicle based on the perception data; classifying the lane lines based on the lane line classification rules to obtain a first lane line and a second lane line, wherein the driving direction of the left lane of the first lane line is consistent with the driving direction of the right lane, and the driving direction of the left lane of the second lane line is inconsistent with the driving direction of the right lane; determining multiple target lane line type points in any grid based on the lane line type points of the first lane line; and combining the multiple target lane line type points to obtain the current frame segmentation line in any grid.

[0013] Furthermore, constructing a lane-level local road map based on the current frame segmentation line includes: determining the midpoint lane line type point of the current frame segmentation line; merging the current frame segmentation line based on the coordinate information of the midpoint lane line type point to obtain a merged lane line; adjusting the lane line type of the merged lane line based on a lane line type rule to obtain an adjusted merged lane line, wherein the lane line type rule is used to merge lane lines with mismatched lane line types into lane lines of solid line type; and constructing a lane-level local road map based on the adjusted current frame segmentation line.

[0014] Furthermore, the current frame segmented lines are merged based on the coordinate information of the midpoint lane line type points to obtain the merged lane line, including: in response to the coordinate distance between adjacent midpoint lane line type points being less than the preset coordinate distance, the current frame segmented lines to which the adjacent midpoint lane line type points belong are merged to obtain the merged lane line.

[0015] Furthermore, based on the precise map data, navigation route information and current lane information, rendering lane-level navigation guide line segments in the lane-level local road map to update the lane-level local road map includes: determining the path to be traveled based on the precise map data, navigation route information, current lane information and merged lane lines; determining the target lane line constituting the path to be traveled based on the merged lane lines; determining the coordinate information of the lane-level navigation guidance point based on the coordinate information of the midpoint lane line point corresponding to the target lane line; generating the lane-level navigation guide line segments based on the coordinate information of the lane-level navigation guidance point; determining the lane boundary line based on the second lane line; rendering the lane-level navigation guide line segments and the lane boundary line in the lane-level local road map to update the lane-level local road map.

[0016] According to another aspect of an embodiment of the present invention, a lane-level local map navigation device is provided, which is applied to a vehicle and includes: an activation module for activating a local map navigation mode in response to receiving a local map navigation activation signal; an acquisition module for acquiring precise map data and perception data, wherein the precise map data is used to provide road network structure data of roads surrounding the vehicle, and the perception data is used to describe the road environment of the roads surrounding the vehicle; a construction module for constructing a lane-level local road map based on the precise map data and the perception data, wherein the lane-level local road map is used to display roads within a first preset distance range ahead of the vehicle that match the navigation route, and the lane-level local road map includes lane lines; a rendering module for rendering lane-level navigation guide line segments in the lane-level local road map based on the precise map data, navigation route information, and information about the vehicle's current lane, wherein the lane-level navigation guide line segments are located within the first preset distance range ahead of the vehicle in a navigation interface, the vehicle's current lane information is lane information in the lane-level local road map, and the lane-level navigation guide line segments are used to provide navigation information for the vehicle in the lane-level local road map; and a navigation module for navigating the vehicle based on the lane-level local road map and the lane-level navigation guide line segments.

[0017] According to another aspect of the embodiments of the present invention, a vehicle is provided for executing the methods in various embodiments of the present invention.

[0018] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0019] According to another aspect of an embodiment of the present invention, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention when running.

[0020] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0021] In an embodiment of the present invention, in response to receiving a local map navigation initiation signal, local map navigation mode is first initiated, and high-precision map data and perception data are acquired. A lane-level local road map is then constructed based on the high-precision map data and perception data. Lane-level navigation guidance lines are then rendered within the lane-level local road map based on the high-precision map data, navigation route information, and the vehicle's current lane. Finally, the vehicle is navigated based on the lane-level local road map and lane-level navigation guidance lines, thereby improving navigation accuracy and enhancing autonomous driving decision support. This provides the technical benefits of lane-level navigation services and addresses the high cost of acquiring and updating high-precision maps, coupled with the inability of high-precision maps to provide lane-level guidance in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 is a flowchart of a navigation method based on a lane-level local map according to an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of a lane-level local road map according to an embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of another lane-level local road map according to an embodiment of the present invention;

[0026] Figure 4 is a schematic diagram of another lane-level local road map according to an embodiment of the present invention;

[0027] Figure 5 is a schematic diagram of a lane line grid division within the longitudinal range of the vehicle according to an embodiment of the present invention;

[0028] Figure 6 is a schematic diagram of dividing valid lane lines into sections and grids according to an embodiment of the present invention;

[0029] Figure 7 is a schematic diagram of lane classification rules according to an embodiment of the present invention;

[0030] Figure 8 is a schematic diagram of sorting, merging and screening local lane lines within a grid according to an embodiment of the present invention;

[0031] Figure 9 2 is a schematic diagram of a navigation device for a lane-level local map according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] According to an embodiment of the present invention, an embodiment of a lane-level local map navigation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] An embodiment of the present application provides a navigation method for a lane-level local map. The navigation method for a lane-level local map can be used to provide navigation functions for preset application scenarios. The above-mentioned preset application scenarios may include the following scenarios in the vehicle field: commuting autonomous driving scenarios, artificial intelligence (AI) driving scenarios for family cars, automatic parking assistance (APA) scenarios (such as memory parking for owned parking spaces in garages, smart parking for designated parking spaces in parking lots, etc.), and intelligent navigation assistance (Navigation Guided Pilot, NGP) scenarios in urban areas or highway areas. In addition, the above-mentioned preset application scenarios may also include, but are not limited to: navigation scenarios for smart driving trucks or unmanned trucks in the field of logistics and transportation, and navigation scenarios for self-driving agricultural vehicles in the field of agricultural machinery.

[0036] When the above-mentioned preset application scenarios are scenarios in fields other than the vehicle field, those skilled in the art should understand that the vehicle in the above-mentioned lane-level local map navigation method can be replaced with other objects (such as agricultural machinery, etc.). On this basis, the embodiments of this application use the field of vehicle-mounted navigation technology as an example to illustrate the specific implementation of the above-mentioned lane-level local map navigation method.

[0037] Figure 1 is a flow chart of a lane-level local map navigation method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0038] Step S10, in response to receiving a local map navigation start signal, starting a local map navigation mode;

[0039] In an embodiment of the present invention, the local map navigation initiation signal is used to instruct the system to begin lane-level local map navigation. For example, the local map navigation initiation signal may originate from a variety of circumstances. For example, when the driver selects the navigation function in the vehicle's multimedia system and selects the "Lane-Level Navigation" option in the menu, the system will receive the local map navigation initiation signal. Alternatively, in autonomous driving mode, the vehicle may automatically determine the need to initiate lane-level navigation based on its own positioning, destination input, or environmental perception results, in which case the local map navigation initiation signal will also be sent to the system. This is not a limitation here.

[0040] The local map navigation mode can be understood as a working mode that the system enters after receiving the local map navigation start signal. For example, in the local map navigation mode, the system will focus on constructing and using lane-level local road maps for navigation, which may include: acquiring and integrating precise map data and real-time perception data to construct a lane-level local road map. Based on the precise map data, the vehicle's current position, driving direction, and navigation route information, lane-level navigation guidance segments are rendered in the constructed lane-level local road map to provide detailed and accurate lane-level navigation information for the driver or the autonomous driving system. The constructed lane-level local road map and the rendered navigation guidance segments are used to continuously and precisely navigate the vehicle to guide it to its destination safely and efficiently, which is not restricted here.

[0041] In response to receiving a local map navigation start signal, turning on the local map navigation mode can be understood as, when the system detects or receives a local map navigation start signal, the system will switch from the conventional navigation mode to the local map navigation mode, that is, from providing road-level navigation information to providing lane-level navigation information and guidance.

[0042] In an embodiment of the present invention, by turning on the local map navigation mode, the navigation accuracy and safety in complex road environments can be improved, allowing the driver or the autonomous driving system to more clearly understand the lane layout, lane line attributes and other important information of the current road, so as to make appropriate lane changes, turns or straight driving operations, and avoid driving errors caused by insufficient information.

[0043] Step S11, obtaining precise map data and perception data, wherein the precise map data is used to provide road network structure data of the roads around the vehicle, and the perception data is used to describe the road environment of the roads around the vehicle;

[0044] In the embodiments of the present invention, the standard map data can be understood as map data that is between traditional low-precision maps and full high-precision maps. It should be noted that the standard map data in the embodiments of the present invention can be understood as a map data set that contains basic road information but with lower detail and accuracy. In other words, the standard map data in the embodiments of the present invention has a small data volume, thus occupying less storage space and saving computing resources.

[0045] For example, the precise map data only provides information such as turning points on the navigation route, distances to turning points, and lane panels at intersections. In other words, the precise map data in this embodiment of the present invention is used to provide road network data surrounding the vehicle. It is understood that in traditional solutions, lane-level navigation guidance cannot be provided using this precise map data.

[0046] Perception data can be understood as the vehicle's surrounding environment information captured and processed in real time by the vehicle's sensor system (including but not limited to cameras, radars, lidars, ultrasonic sensors, etc.). For example, perception data may include: the position, speed, and behavior of dynamic obstacles around the vehicle, such as other vehicles, pedestrians, bicycles, etc. The status and position of lane lines, including the degree of wear of the lane lines, whether they are blocked, whether they are temporarily changed, etc. Information on traffic signs and signals, such as speed limits, no lane changes, traffic light status, etc. Road conditions, such as slippery, potholes, construction, etc., are not restricted here.

[0047] Perception data is used to describe the road environment around the vehicle. It can be understood that perception data is collected in real time by sensors on the vehicle and reflects the current status of the vehicle's surrounding environment, including dynamic obstacles (such as other vehicles, pedestrians, animals), the specific status of lane lines (such as whether there are temporary lane change signs, whether the lane lines are clearly visible), road conditions (such as wet, damaged), etc.

[0048] Obtaining precise map data and perception data can be understood as the system retrieving precise map data from the map database, and the system activating various sensors on the vehicle to continuously collect real-time perception data of the vehicle's surroundings.

[0049] As can be seen, standard maps only provide basic data such as turning points, distance information, and intersection lane panels, but cannot provide detailed lane-level navigation information. This invention supplements lane-level detailed information with real-time perception data, making the navigation guidance generated by combining the two more accurate and significantly improving the user experience.

[0050] In this embodiment of the present invention, by acquiring both precise map data and real-time perception data, costs are reduced due to the low storage requirements and computing resources used for precise map data. Furthermore, acquiring real-time perception data effectively overcomes the limitations of a single data source, making lane-level navigation more accurate and real-time, significantly enhancing the adaptability and reliability of the navigation system. This provides drivers or autonomous driving systems with safer and more detailed guidance information, particularly in complex and changing road conditions, reducing accident risks and improving driving efficiency. Furthermore, acquiring real-time perception data enables the capture of unexpected situations not captured in precise map data, such as the sudden appearance of static obstacles, pedestrians, or non-motorized vehicles. This allows for more effective identification and response to potential hazards, enabling the implementation of necessary avoidance measures, and significantly improving driving safety. In other words, by combining precise map data with real-time perception data, this embodiment of the present invention maintains low cost and maintenance requirements while improving the real-time and safety of navigation, enhancing the user experience, and providing more detailed and flexible data support for subsequent route planning.

[0051] Step S12: constructing a lane-level local road map based on the precise map data and the perception data, wherein the lane-level local road map is used to display roads within a first preset distance range ahead of the vehicle that match the navigation route, and the lane-level local road map includes lane markings;

[0052] In an embodiment of the present invention, the map data structure of the lane-level local road map mainly includes: the current number of lanes, the current left and right road boundaries, the lane line attribute type, the lane index where the vehicle is located, and other information, which are not limited here. Among them, the current number of lanes can be understood as the total number of lanes in the road segment where the vehicle is currently located. For example, if the vehicle is traveling on a road with three lanes, the current number of lanes is 3. The current left and right road boundaries can be understood as the boundary information on the left and right sides of the road segment where the vehicle is located. The lane line attribute type includes the lane line type (such as dotted line, solid line), color, and other attributes. The lane index where the vehicle is located can be understood as a numerical value used to indicate which lane the vehicle is currently traveling in. Usually, lanes are numbered from left to right. For example, if the current number of lanes is 3 and the vehicle is traveling in the rightmost lane, the lane index where the vehicle is located is 2 (assuming the count starts at 0).

[0053] The front of the vehicle can be understood as the front of the vehicle's driving direction, which may include turning, going straight, U-turning and other situations, which are not restricted here.

[0054] The first preset distance range can be understood as the road within a certain distance range in front of the vehicle (not extending to the end of the road). For example, the first preset distance range can be the road within a range of 0m-80m in front of the vehicle, or the first preset distance range can be the road within a range of 0m-120m in front of the vehicle, which is not limited here. Figure 2 is a schematic diagram of a lane-level local road map according to an embodiment of the present invention, such as Figure 2 As shown in the figure, the lane-level local road map shows the road within a certain distance in front of the vehicle. That is, the road in front of the vehicle shown in the lane-level local road map does not extend to the end of the road. Therefore, the specific lane position of the vehicle can be accurately determined based on the lane-level local road map, thereby making more accurate driving decisions. In addition, the lane-level local road map also includes lane line information, which helps to better understand the current road environment. Figure 2 The rest of the description is given below.

[0055] Building a lane-level local road map based on precision map data and perception data can be understood as generating a lane-level road map based on the vehicle's real-time position, using the stable road structure background provided by the precision map and combining it with the immediate environmental status fed back by perception data.

[0056] In an embodiment of the present invention, by integrating the standard precision map and the perception data, a lane-level local road map of a certain distance in front of the vehicle is constructed, and details such as lane lines are accurately depicted, thereby significantly improving navigation accuracy and safety. In particular, in complex road conditions, strong support is provided for driving decisions, efficient path planning is achieved, and potential accident risks are reduced. At the same time, the lane-level local road map contains lane line information, which helps the autonomous driving system or the driver to better understand the current road environment, especially at complex intersections or road junctions, and accurately judge the continuity and connectivity of the lanes to avoid incorrect path planning or lane changing operations. In addition, compared to the traditional sense, lane-level navigation requires reliance on high-precision maps, and the production and maintenance costs of high-precision maps are very high. The present invention constructs a lane-level local road map, which can achieve lane-level navigation functions even in the absence of high-precision maps, greatly reducing application costs and data processing complexity.

[0057] Step S13: Rendering a lane-level navigation guide line segment in the lane-level local road map based on the precise map data, the navigation route information, and the vehicle's current lane information, wherein the lane-level navigation guide line segment is located within a first preset distance range in front of the vehicle in the navigation interface, and the vehicle's current lane information is the vehicle's current lane information in the lane-level local road map. The lane-level navigation guide line segment is used to provide navigation information for the vehicle in the lane-level local road map.

[0058] In an embodiment of the present invention, the navigation route information can be understood as a navigation route determined based on the user's destination address. The navigation route information is not directly displayed on the lane-level local road map, but is data pre-calculated by the cloud based on the destination address and the current position of the vehicle. That is, the navigation route information is data directly obtained by the vehicle from the cloud.

[0059] Lane-level navigation guidance lines are visual elements that provide drivers or autonomous driving systems with specific lane-level navigation guidance on lane-level local road maps. Lane-level navigation guidance lines are rendered within a first preset distance range in front of the vehicle in the navigation interface based on precise map data, the vehicle's current navigation route, and the specific lane information in which the vehicle is located. Lane-level navigation guidance lines can intuitively indicate how the vehicle should drive in the current and forward lanes, including but not limited to when to change lanes, which lane to change to, and which lane to continue driving in. Lane-level navigation guidance lines make navigation guidance more refined and intuitive, helping drivers or autonomous driving systems to more accurately understand and execute navigation instructions, especially in scenarios such as multi-lane and complex intersections. They can significantly improve the effectiveness and safety of navigation and reduce driving errors caused by misunderstanding navigation information.

[0060] For example, Figure 2As shown, the lane-level navigation guidance line segments are rendered within a first preset distance range in front of the vehicle and do not extend to the entire edge of the in-vehicle large screen. In other words, the lane-level navigation guidance line segments are only rendered on the road within a certain distance range in front of the vehicle displayed on the lane-level local road map, thereby ensuring that the navigation guidance is both effective and non-interfering. It is understandable that the length of the lane-level navigation guidance line segment is limited. The length of the lane-level navigation guidance line segment can correspond to an actual road distance of 80m-120m. This is only an example and is not limited. Figure 2 The rest of the description is given below.

[0061] Based on precise map data, navigation route information, and the vehicle's current lane, lane-level navigation guidance lines are rendered within the lane-level local road map. This means the system uses precise map data as the underlying road network framework, combined with the vehicle's real-time navigation route and current lane information, to dynamically draw lane-level navigation guidance lines within the lane-level local road map. These lane-level guidance lines not only indicate the optimal driving path for the vehicle but also emphasize lane-level details, such as when and where to change lanes, as well as the target lane.

[0062] For example, Figure 3 and Figure 4 are schematic diagrams of two different lane-level local road maps according to embodiments of the present invention, Figure 3 and Figure 4 It shows the situation where the road edge changes during the vehicle's driving process, where Figure 3 This shows a scenario where the road edge is about to change while the vehicle is driving. The current number of lanes is 3. Figure 4 The figure shows a scene where the road side has changed while the vehicle is driving. At this time, a new lane is added to the left side of the vehicle, and the current number of lanes is 4. Figure 3 In the scenario shown, the lane-level navigation guidance line segment instructs the vehicle to drive to the third lane from left to right on a three-lane road. Figure 4 In the scenario shown, the lane-level navigation guidance line segment instructs the vehicle to drive to the third lane from left to right on a four-lane road. Figure 3 The scene shown changes to Figure 4 In the scenario shown, the lane-level navigation guidance line segments will be in a jittering state, that is, during this process, the lane-level navigation guidance line segments will be continuously updated according to the real-time road conditions so that they can guide the vehicle to the optimal driving path.

[0063] In an embodiment of the present invention, through the above steps, navigation information is converted into intuitive visual guidance and displayed directly within a preset distance range in front of the vehicle, greatly improving the accuracy and ease of navigation, helping the driver or the autonomous driving system to make timely and correct decisions. Especially in complex traffic environments, lane-level navigation guidance segments can significantly enhance the safety and smoothness of driving.

[0064] Step S14: Navigate the vehicle based on the lane-level local road map and the lane-level navigation guidance line segments.

[0065] In an embodiment of the present invention, navigating a vehicle based on a lane-level local road map and lane-level navigation guidance segments can be understood as using a high-precision lane-level local road map and detailed lane-level navigation guidance segments to provide accurate path guidance for the vehicle.

[0066] In an embodiment of the present invention, a lane-level local road map is used to carefully depict the lane layout, lane line attributes and other information of the vehicle's surrounding environment. Combined with lane-level navigation guidance segments, it clearly indicates the lane path that the vehicle should follow in current and subsequent driving. This can effectively guide the vehicle to make correct lane changes or driving decisions in situations such as multi-lane and complex intersections, greatly improving navigation accuracy, safety and user experience.

[0067] As can be seen, traditional solutions use high-precision maps to provide lane-level guidance. The production and maintenance of high-precision maps requires significant resources, involving complex data collection and real-time update mechanisms, resulting in high costs and limited update frequency. In contrast, the proposed standard-precision map, combined with a pure visual solution, relies on the vehicle's own perception capabilities to obtain lane-level details in real time. This significantly reduces reliance on high-precision map data and can quickly adapt to road changes, allowing for widespread application in diverse regions without concerns about map update lags impacting navigation performance. Furthermore, due to the long update cycle of traditional high-precision maps, they may not immediately reflect sudden changes in road conditions (such as temporary construction or road closures). In contrast, the proposed solution leverages real-time vehicle perception data to instantly capture and analyze these changes, dynamically adjusting the content of the lane-level local road map. This ensures that lane-level navigation guidance segments accurately reflect the current road conditions, improving navigation's real-time responsiveness and flexibility, and ensuring that driving decisions are based on the latest and most accurate information. In addition, by combining accurate maps with pure visual solutions, the present invention enables lane-level navigation guidance lines to be displayed intuitively on the navigation interface. This not only reduces the driver's cognitive load, allowing the driver to focus more on driving itself, but also improves the transparency of operations in autonomous driving mode, allowing users to have greater trust in the system's capabilities, thereby improving the overall user experience.

[0068] In an embodiment of the present invention, in response to receiving a local map navigation initiation signal, local map navigation mode is first initiated, and high-precision map data and perception data are acquired. A lane-level local road map is then constructed based on the high-precision map data and perception data. Lane-level navigation guidance lines are then rendered within the lane-level local road map based on the high-precision map data, navigation route information, and the vehicle's current lane. Finally, the vehicle is navigated based on the lane-level local road map and lane-level navigation guidance lines, thereby improving navigation accuracy and enhancing autonomous driving decision support. This provides the technical benefits of lane-level navigation services and addresses the high cost of acquiring and updating high-precision maps, coupled with the inability of high-precision maps to provide lane-level guidance in related technologies.

[0069] Optionally, the lane-level local road map is also used to display roads within a second preset distance range behind the vehicle, where the area of ​​the second preset distance range is smaller than the area of ​​the first preset distance range.

[0070] In the embodiment of the present invention, the second preset distance range can be understood as a specific distance interval behind the vehicle, whose area or coverage is smaller than the first preset distance range in front of the vehicle. For example, the second preset distance range can be a road within a range of 0m-5m behind the vehicle, or the second preset distance range can be a road within a range of 0m-10m behind the vehicle, which is not limited here. Figure 2 As shown, the lane-level local road map not only displays the roads within a first preset distance range in front of the vehicle, but also the roads within a second preset distance range behind the vehicle, and the area of ​​the second preset distance range is smaller than the area of ​​the first preset distance range. Therefore, by simultaneously presenting the road information in front of and behind the vehicle on the map, the driver or the autonomous driving system can obtain a more comprehensive environmental perception, which not only helps to predict the road conditions ahead, but also monitors potential traffic conditions behind, such as the closing speed and position changes of the rear vehicle, and thus make safer driving decisions. Figure 2 The rest of the description is given below.

[0071] The lane-level local road map is also used to display the roads within a second preset distance range behind the vehicle. It can be understood that the lane-level local road map not only provides detailed lane information in front of the vehicle, but also covers the road conditions within a certain distance behind the vehicle, so that the driver or the autonomous driving system can refer to the traffic environment in front and behind at the same time to make more comprehensive and safe driving decisions.

[0072] The second preset distance range being smaller than the first preset distance range indicates that, in the lane-level local road map, the road information coverage behind the vehicle is smaller than that in front of the vehicle. By setting a smaller rear distance range, the system can focus more resources on collecting and processing information in front of the vehicle while also ensuring that necessary information from behind is also considered, achieving more efficient and safer navigation and driver assistance functions.

[0073] In the embodiment of the present invention, through the above steps, not only can the lane details within a first preset distance in front of the vehicle be displayed, but also the road information within a second preset distance range behind the vehicle can be covered. The area of ​​the rear range is smaller, which enhances the driver's all-round field of vision, especially in scenarios where attention needs to be paid to traffic dynamics behind, such as merging or reversing, thereby improving the practicality and safety of navigation and ensuring the comprehensiveness and accuracy of driving decisions.

[0074] Optionally, the starting point of the lane-level navigation guidance line segment is located at the front position of the vehicle in the navigation interface, and the end point of the lane-level navigation guidance line segment is located on the road within a first preset distance range in front of the vehicle that matches the navigation route in the navigation interface, and the display area of ​​the lane-level local road map is smaller than the display area of ​​the vehicle navigation interface.

[0075] In an embodiment of the present invention, the starting point of the lane-level navigation guidance line segment is located at the front position of the vehicle in the navigation interface. It can be understood that the drawing of the lane-level navigation guidance line segment starts from the front of the vehicle's current actual position, ensuring that the navigation information is closely related to the real-time status of the vehicle and providing accurate driving direction guidance.

[0076] The end point of the lane-level navigation guidance line segment is located on the road within the first preset distance range in front of the vehicle that matches the navigation route in the navigation interface. It can be understood that the line segment extends to the road at a set distance in front of the vehicle. This distance is sufficient to cover key driving decision points such as upcoming turning points and forks in the road, providing sufficient reaction time for the driver or the autonomous driving system.

[0077] The display area of ​​the lane-level local road map is smaller than that of the vehicle navigation interface. This can be understood as the local map only focuses on the most relevant and important road information areas around the vehicle. Its purpose is to optimize information display and reduce unnecessary visual interference, so that the driver or the system can quickly and accurately obtain and process key lane-level navigation data, thereby improving driving safety and efficiency. Figure 2As shown, it can be seen that the existing in-vehicle navigation software interface (usually showing the vehicle's driving path from a bird's-eye view and not showing specific road conditions) and the lane-level local road map of the present invention can be displayed simultaneously on the in-vehicle large screen, and the display area of ​​the lane-level local road map is smaller than the display area of ​​the vehicle navigation interface, thereby optimizing information display, reducing unnecessary visual interference, and reducing the amount of calculation for generating the map, so that the lane-level local road map can be generated and displayed more efficiently.

[0078] In this embodiment of the present invention, lane-level navigation guidance segments are positioned at the vehicle's front and extend to roads matching the navigation route within a predetermined distance ahead, ensuring immediacy and foresight in driving decisions. Furthermore, the reduced display area of ​​the local road map optimizes the information density of the navigation interface, highlighting key lane information and reducing visual clutter. This improves navigation intuitiveness and driving safety, effectively assisting the driver or autonomous driving system in responding quickly and accurately, particularly in complex traffic environments.

[0079] Optionally, in step S12, constructing a lane-level local road map based on the precise map data and the perception data includes the following steps:

[0080] Step S121, determining a plurality of grids in the vehicle coordinate system according to the standard map data;

[0081] Step S122, determining the lane line point corresponding to the current frame based on the perception data;

[0082] Step S123: Mapping the lane line points of the current frame to multiple grids in the vehicle coordinate system to obtain a mapping relationship between the lane line points of the current frame and the multiple grids, wherein the mapping relationship is used to record the grid to which any lane line point of the current frame belongs;

[0083] Step S124, in any grid, combining the lane line points of the current frame in any grid according to the mapping relationship to obtain the segmentation line of the current frame in any grid;

[0084] Step S125: construct a lane-level local road map based on the segmentation lines of the current frame.

[0085] In the embodiments of the present invention, the ego-vehicle coordinate system can be understood as a coordinate system centered on the ego-vehicle (i.e., the vehicle being driven by the user). In the ego-vehicle coordinate system, the vehicle's position is defined as the origin (0, 0), and the vehicle's direction of travel is defined as the positive direction of the coordinate axis, which is typically aligned with the vehicle's longitudinal axis.

[0086] The lane line point of the current frame can be understood as the lane line data captured at the current time point.

[0087] The current frame segmentation line is obtained by mapping the lane line points of the current frame to multiple grids in the vehicle coordinate system, dividing the points into grids, and combining the points in each grid.

[0088] Determining multiple grids in the ego-vehicle coordinate system based on precise map data can be understood as leveraging road information from the precise map data, such as lane widths and road boundaries, combined with the vehicle's position to define a series of grids corresponding to the vehicle's location and covering the vehicle's surroundings. These grids are arranged in the ego-vehicle coordinate system for easy updating and management as the vehicle moves, ensuring rapid extraction and processing of road information based on the vehicle's current and future travel paths.

[0089] Determining the lane line type points corresponding to the current frame based on perception data can be understood as identifying the shape and position of the lane lines around the vehicle through the data collected in each frame by the sensors of the autonomous driving system (such as cameras and radars), and converting them into multiple key points, namely lane line type points. The lane line type points of the current frame carry detailed attributes of the lane lines, such as type and color, for subsequent gridding processing and lane modeling.

[0090] Mapping the lane line points of the current frame to multiple grids in the ego vehicle coordinate system, and obtaining the mapping relationship between the lane line points of the current frame and the multiple grids can be understood as accurately assigning the lane line points identified and extracted above to the corresponding grids according to their positions in the ego vehicle coordinate system, forming a point-to-grid correspondence table, ensuring that the lane line information contained in each grid is the actual situation around the vehicle in the current frame, which facilitates the subsequent grid-based processing of the lane line information. Figure 5 and Figure 6 As shown in the figure, the area from 5m behind the vehicle to 100m in front or to the next intersection is divided into grids with intervals of 10m. By capturing lane line points in real time, the lane line points are placed in the nearest grid according to their coordinates. In this way, it is ensured that the information included in each grid reflects the instantaneous lane conditions around the vehicle, laying a data foundation for subsequent grid processing, such as sorting, merging and screening of lane lines, and ultimately facilitating the generation of lane-level navigation guidance, improving driving safety and the accuracy of autonomous driving. At the same time, through this mapping, the system can process lane-level information more efficiently, reduce computing load, and maintain the real-time and accuracy of the data. For Figure 5 and Figure 6 The rest of the description is given below.

[0091] In any grid, the lane line points in the current frame are combined according to the mapping relationship to obtain the current frame segmentation line for that grid. This can be understood as combining the lane line points mapped to each grid to form a line segment representing the lane line characteristics within that grid, namely the current frame segmentation line. This segmentation line can concisely and accurately describe the local attributes and direction of the lane line, facilitating lane-level map construction and subsequent driving decisions.

[0092] Constructing a lane-level local road map based on the current frame's segmentation lines can be understood as using the generated current frame segmentation lines to construct a small map containing detailed information about the lanes surrounding the vehicle, including lane width, lane line attributes, and the vehicle's position relative to the lane. The lane-level local road map is dynamically updated, ensuring accurate lane-level navigation and driver assistance information throughout the vehicle's journey. This is crucial for achieving autonomous driving and enhancing driving safety.

[0093] In this embodiment of the present invention, accurate map data is converted to the vehicle coordinate system and divided into multiple grids. Lane line points captured by the perception system are then mapped to these grids, forming a mapping relationship. Based on this mapping relationship, the line points within each grid are integrated into segmented lines, ultimately constructing a real-time lane-level local road map. This effectively integrates static map and dynamic perception information, significantly improving navigation accuracy and response speed. Especially in complex road environments, it can provide drivers or autonomous driving systems with more intuitive and timely lane-level navigation guidance, enhancing driving safety and the navigation user experience.

[0094] Optionally, in step S121, determining a plurality of grids in the vehicle coordinate system according to the precise map data includes the following steps:

[0095] Step S1211: Determine a grid division range of the vehicle coordinate system based on the accurate map data, wherein the grid division range is from a second preset distance behind the vehicle in the direction of travel to the first fork in the road or a third preset distance in the direction of travel;

[0096] Step S1212, determining the location to be divided based on the preset division interval and the road segment dividing point in the grid division range;

[0097] Step S1213 : Divide the vehicle coordinate system based on the position to be divided to obtain multiple grids in the vehicle coordinate system.

[0098] In the embodiment of the present invention, the grid division range is the spatial area used to construct the lane-level local map in the vehicle coordinate system, and is also the range that the vehicle can perceive.

[0099] The preset division interval can be understood as the maximum grid width, for example, 10m interval, which is not limited here.

[0100] The road segment dividing point can be understood as the starting point and end point of a road segment in a road network, marking the boundary of a continuous section of road with the same attributes.

[0101] The positions to be divided can be understood as a series of spatial coordinate points pre-calculated or determined in the vehicle coordinate system according to the preset division intervals and road segment dividing points. These points indicate the specific locations where the grid division should start or end.

[0102] Determining the gridding range of the vehicle coordinate system based on standard-precision map data can be understood as defining an area centered on the vehicle's position and covering a certain distance ahead and behind, based on the road information provided by the standard-precision map, including road boundaries and lane layout. This area is then used for subsequent gridding. The gridding range is set not only based on the vehicle's current driving state but also on potential traffic conditions ahead, such as forked intersections, to ensure that the gridding incorporates key information along the vehicle's path.

[0103] The grid division range is from the second preset distance behind the vehicle's direction of travel to the first fork in the road or the third preset distance in front of the vehicle's direction of travel. This can be understood as determining a dynamic grid division area, where the starting point of the area is located at a certain distance behind the vehicle (the second preset distance) and the ending point depends on the first fork in the road or a preset distance in front of the vehicle (the third preset distance, which is usually greater than the second preset distance). For example, the grid division range can start from 5 meters behind the vehicle and end at the first fork in the road or 100 meters in front of the vehicle. For example, if there is an intersection 100 meters away on an urban road section, the intersection is the end point; if there is no intersection 100 meters away on a highway section, the 100-meter point is the end point. This is not limited here.

[0104] Determining the location to be divided based on the preset division intervals and the road section dividing points in the grid division range can be understood as determining a series of division locations or coordinate points within the defined grid division range according to the preset interval distances and key points on the road (such as road section dividing points, which may mark changes in the number of lanes or lane line attributes).

[0105] Dividing the vehicle coordinate system based on the positions to be divided to obtain multiple grids in the vehicle coordinate system can be understood as using the above-determined division positions to divide the environment around the vehicle into multiple grids, each grid representing road information within a specific distance.

[0106] In this embodiment of the present invention, the aforementioned gridding method enables a structured description of the road environment in the ego-vehicle coordinate system, facilitating subsequent processing and analysis, such as the generation of lane-level navigation guidance and the autonomous vehicle's perception and decision-making regarding road conditions. This gridding method effectively manages the complexity of road information while ensuring the availability and flexibility of critical data updates.

[0107] Optionally, determining the location to be divided based on the preset division interval and the road segment dividing point in the grid division range includes the following steps:

[0108] In response to a road segment dividing point existing in the grid division range, determining a first division position based on a preset division interval within a first range, wherein the first range is from a second preset distance behind the vehicle in the direction of travel to the road segment dividing point;

[0109] Determining a second division position based on a preset division interval within the second range, wherein the second range is from the road segment dividing point to the first fork in the vehicle's travel direction or a third preset distance ahead;

[0110] The position to be divided is determined based on the first division position and the second division position.

[0111] In the embodiment of the present invention, the first range can be understood as a road area starting from a second preset distance behind the vehicle in the driving direction and extending to the first road segment dividing point encountered.

[0112] The second range can be understood as an area starting from the first road section dividing point and continuing forward until the first fork intersection appears or reaches a third preset distance.

[0113] The first division position can be understood as a coordinate point determined within the first range along the longitudinal direction of the road according to a preset division interval (such as every 10 meters), which is not limited here.

[0114] The second division position can be understood as a coordinate point determined according to the same preset division interval within the second range. The second division position and the first division position together constitute all the positions to be divided in the grid division, which is not limited here.

[0115] In response to the existence of a road section dividing point in the grid division range, determining the first division position based on the preset division interval within the first range can be understood as follows: when a road section dividing point is detected within the grid division range, the system will start from the rear of the vehicle and along the direction of vehicle travel, within the first range (i.e., the road section from the second preset distance behind the vehicle to the road section dividing point), determine a series of position points according to the preset division interval (such as 5 meters, 10 meters, etc.), and these position points are called first division positions.

[0116] Determining the second division position based on the preset division interval within the second range can be understood as, after the grid division of the first range is completed, the system then starts from the road section dividing point to the first fork intersection in front of the vehicle or the third preset distance (ie the second range), and also determines the second division position based on the preset division interval.

[0117] Determining the position to be divided based on the first division position and the second division position can be understood as the system combining the first division position and the second division position to form the starting point and the end point of the grid division, that is, the position to be divided.

[0118] In this embodiment of the present invention, based on a gridded range containing road segment demarcation points, a first and second division location are determined at preset intervals, and then the location to be divided is determined. By dynamically adapting the grid to changing road characteristics, the efficiency and accuracy of local road network mapping are significantly improved, providing real-time, detailed road information for lane-level navigation and autonomous driving strategies. This avoids the high cost of high-precision maps, enhancing the system's flexibility and cost-effectiveness.

[0119] Figure 5 Schematic diagram of lane line grid division in the longitudinal range of the vehicle according to an embodiment of the present invention, as shown in FIG. Figure 5 As shown, the maximum grid width is set to 10m, and the start and end points of the longitudinal grid division are set. The starting point is 5m behind the vehicle, and the end point is the distance between the vehicle and the next intersection (i.e., V2 Stub data, a type of data in vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) communications, where V2 Stub data refers to data associated with the vehicle and the next intersection (Stub)). The maximum forward distance is 100m. The division strategy is to divide the grid sequentially from the starting point at 10m intervals. If a segment demarcation point (i.e., the specific location where a segment begins and ends) is encountered in the V2 Stub data, the grid is divided at the segment demarcation point until the grid ends.

[0120] Optionally, determining the location to be divided based on the preset division interval and the road segment dividing point in the grid division range includes the following steps:

[0121] In response to the presence of a plurality of road segment dividing points in the grid division range, determining a third division position within the third range based on a preset division interval, wherein the plurality of road segment dividing points include a first road segment dividing point and a second road segment dividing point, the first road segment dividing point is closer to the vehicle than the second road segment dividing point, and the third range is from a second preset distance behind the vehicle in the direction of travel to the first road segment dividing point;

[0122] Determining a fourth dividing position based on a preset dividing interval within a fourth range, wherein the fourth range is from the first road section dividing point to the second road section dividing point;

[0123] Determining a fifth division position based on a preset division interval within a fifth range, wherein the fifth range is from the second road segment dividing point to the first fork in the road ahead of the vehicle or a third preset distance;

[0124] The position to be divided is determined based on the third divided position, the fourth divided position, and the fifth divided position.

[0125] In the embodiment of the present invention, the third range can be understood as an area starting from the second preset distance behind the vehicle's traveling direction and ending at the first road section dividing point encountered (ie, the first road section dividing point).

[0126] The third division position can be understood as a coordinate point determined within the third range according to a preset division interval, which is used to indicate the starting point or end point of the grid division to ensure an accurate description of the environment behind the vehicle.

[0127] The first road section dividing point can be understood as the point where the road network structure changes, such as the increase or decrease in the number of lanes, the road turning point, etc., which is closest to the vehicle in the forward driving direction of the vehicle. There is no restriction here.

[0128] The second road section dividing point can be understood as the next key point where structural changes occur on the road after the first road section dividing point, and is used to define the transition between the fourth range and the fifth range.

[0129] The fourth range can be understood as a road area located after the first road section dividing point and before the second road section dividing point.

[0130] The fourth division position can be understood as a coordinate point determined within the fourth range based on the same preset division interval, which is used to guide grid division to ensure accurate capture of road features between the dividing points of the first and second sections, and is not limited here.

[0131] The fifth range can be understood as starting from the second road segment dividing point and extending to the first fork in the road ahead of the vehicle or the third preset distance.

[0132] The fifth division position can be understood as a coordinate point determined along a preset division interval within the fifth range, which is used to subdivide the grid to ensure a detailed depiction of distant road information, such as the lane layout at a fork, etc., and is not limited here.

[0133] In response to the presence of multiple road section dividing points in the grid division range, determining the third division position based on the preset division interval within the third range can be understood as follows: when the system identifies multiple road section dividing points, within the road area (third range) between the second preset distance behind the vehicle and the nearest first road section dividing point, the starting and ending points of the grid division are set one by one according to the preset division interval, which is the third division position.

[0134] Determining the fourth division position based on the preset division interval within the fourth range can be understood as, within the intermediate section (i.e., the fourth range) extending from the first section dividing point to the next section dividing point (the second section dividing point), also setting the grid division position according to the preset interval distance, i.e., the fourth division position.

[0135] Determining the fifth division position based on the preset division interval within the fifth range can be understood as, within the road range from the second road section dividing point to the first fork intersection in front of the vehicle or the third preset distance (i.e., the fifth range), the grid division node determined according to the preset interval is the fifth division position.

[0136] Determining the position to be divided based on the third, fourth and fifth dividing positions can be understood as forming a complete grid division coordinate system, namely the position to be divided, by integrating the third, fourth and fifth dividing positions.

[0137] In an embodiment of the present invention, based on the proposed multi-division point grid division strategy, the present invention can accurately place the division positions between the division points of different road sections, avoiding heavy reliance on high-precision maps, utilizing the vehicle's existing perception and navigation data, reducing the cost of map updates and maintenance, while maintaining high mapping accuracy and efficiency.

[0138] Optionally, in any grid, combining the lane line points of the current frame in any grid according to the mapping relationship to obtain the segmented line of the current frame in any grid includes the following steps:

[0139] determining lane lines in a road around the vehicle based on the perception data;

[0140] Classifying the lane lines based on a lane line classification rule to obtain a first lane line and a second lane line, wherein a left lane driving direction of the first lane line is consistent with a right lane driving direction, and a left lane driving direction of the second lane line is inconsistent with a right lane driving direction;

[0141] Determine a plurality of target lane line-type points in any grid based on the lane line-type point of the first lane line;

[0142] Combine multiple target lane line points to obtain the current frame segmentation line in any grid.

[0143] In the embodiment of the present invention, the lane line classification rule can be understood as a method criterion for distinguishing lane lines based on the attributes of the lane lines in the vehicle perception data (such as the direction, type and connectivity of the lane lines).

[0144] The first lane line can be understood as the lane line where the left and right lanes have the same driving direction.

[0145] The second lane line can be understood as the lane line where the driving directions of the left and right lanes are inconsistent, such as the dividing line between lanes, one side of which may be a left-turn or right-turn lane, while the other side is a straight or other turning lane.

[0146] For example, Figure 7 is a schematic diagram of lane classification rules according to an embodiment of the present invention, such as Figure 7 As shown, taking the implementation of lane lines as an example for explanation, the direction indicated by the arrow is the driving direction of the lane. The first lane line is the lane line where the driving directions of the left and right lanes are the same, and the second lane line is the lane line where the driving directions of the left lane and the right lane are inconsistent.

[0147] Determining lane lines on the roads around the vehicle based on perception data can be understood as using the vehicle's perception elements (such as cameras, radars, etc.) and autonomous driving deep learning algorithms to analyze and identify lane line information in the vehicle's surrounding environment in real time, including the position, shape, and attributes of the lane lines (such as dotted lines, solid lines, colors, etc.), laying the foundation for subsequent lane line classification and map construction.

[0148] Classifying lane lines based on lane classification rules to obtain first and second lane lines can be understood as categorizing perceived lane lines into two types based on their direction and traffic flow characteristics. First lane lines specifically refer to lanes where the travel direction of both lanes is consistent, typically representing lanes traveling in the same direction. Second lane lines, on the other hand, represent lanes where the travel direction of both lanes is inconsistent, such as road boundaries or lane dividers separating lanes with different travel directions. This information helps accurately describe the structure and layout of the road.

[0149] Determining multiple target lane linetype points in any grid based on the lane linetype points of the first lane line can be understood as selecting key lane linetype points from the first lane line, such as the starting point, midpoint, and end point, further processing and analyzing the key lane linetype points, and determining multiple target points representing lane line characteristics in each grid in the gridded map based on the key lane linetype points. The above target points will be used to describe and construct lane information in the vehicle's driving direction.

[0150] Figure 6 FIG. 1 is a schematic diagram of dividing the effective lane lines into sections and grids according to an embodiment of the present invention. Figure 6As shown, exemplarily, a binary search algorithm or a traversal algorithm is used to traverse the lane points on the valid lane line, determine whether the longitudinal coordinates of the lane line point are within the grid division range, find the grid corresponding to the lane line point, and form a local lane line with the lane line points and place it in the grid. To simplify the data, the local lane line only saves the starting point, end point, midpoint, lane type, and whether it is the left lane boundary or the right lane boundary, which are not restricted here.

[0151] Combining multiple target lane line points to obtain the current-frame segmentation line in any grid can be understood as connecting and combining all target lane line points within the same grid according to a specific algorithm to form a segmentation line that describes the local road conditions. This segmentation line represents the actual layout and characteristics of the lane lines within each grid at the current moment (i.e., the current frame), providing real-time, accurate map data support for subsequent lane change strategies and path planning.

[0152] In an embodiment of the present invention, a lane-level local map of the road around the vehicle is constructed by utilizing perception data, classifying lane lines, selecting lane line points and combining them into segment lines. This provides key environmental perception information for the autonomous driving system, helping to improve driving safety and efficiency.

[0153] Optionally, constructing a lane-level local road map based on the current frame segmentation line includes:

[0154] Determine the midpoint lane line point of the current frame segment line;

[0155] Merge the segmented lines of the current frame based on the coordinate information of the midpoint lane line point to obtain a merged lane line;

[0156] adjusting the lane line type of the merged lane line based on the lane line type rule to obtain an adjusted merged lane line, wherein the lane line type rule is used to merge lane lines with mismatched lane line types into a solid line type lane line;

[0157] Construct a lane-level local road map based on the adjusted segmentation lines of the current frame.

[0158] In the embodiment of the present invention, the midpoint lane line point can be understood as a representative point selected in the current frame segment line to describe the lane line characteristics, and this point is located at the midpoint of the segment line.

[0159] Determining the midpoint lane line point of the current frame segmentation line can be understood as selecting a lane line point located in the middle of the current frame segmentation line in each grid as the representative point of the lane line.

[0160] The merged lane line is obtained by merging the segmented lines of the current frame based on the coordinate information of the midpoint lane line type points. This can be understood as comparing the horizontal coordinates of the midpoint type points of each segmented line. If the horizontal coordinates of the midpoint type points of two or more segmented lines are similar, these lane lines may belong to the same lane or adjacent lanes within the grid. In this case, these segmented lines can be merged into a longer lane line (i.e., a merged lane line) to more accurately depict the actual road structure. If the lane line types do not match, the type adjustment is performed based on the principle of solid line priority.

[0161] Adjusting the lane line type of the merged lane line based on the lane line type rule. The adjusted merged lane line can be understood as follows: in the process of merging lane lines, if inconsistent lane line types are encountered (for example, a dashed line and a solid line), according to the preset lane line type rule (for example, a solid line type lane line has a higher priority than a dashed line), the merged lane line will be uniformly adjusted to a type with a higher priority (usually a solid line) to ensure the consistency and accuracy of the map data.

[0162] Constructing a lane-level local road map based on the adjusted current frame segmentation lines can be understood as taking the adjusted merged lane lines as input data and using a suitable data structure to construct a lane-level local minimap describing the road conditions around the vehicle after completing the lane line merging and type adjustment.

[0163] In an embodiment of the present invention, the above steps integrate all lane line information to form an orderly, coherent and easy-to-understand map model, providing real-time lane-level navigation information to the autonomous driving system, supporting the autonomous driving system to make reasonable path planning and driving decisions.

[0164] Optionally, merging the segmented lines of the current frame based on the coordinate information of the midpoint lane line point to obtain the merged lane line includes the following steps:

[0165] In response to a coordinate distance between adjacent midpoint lane line type points being less than a preset coordinate distance, the current frame segment lines to which the adjacent midpoint lane line type points belong are merged to obtain a merged lane line.

[0166] In an embodiment of the present invention, in response to the coordinate distance between adjacent midpoint lane line type points being less than a preset coordinate distance, the current frame segmented lines to which the adjacent midpoint lane line type points belong are merged to obtain a merged lane line. This can be understood as follows: based on the lane line grid division, by calculating the coordinate distance of the midpoint lane line type points within the same grid or across grids, if the lateral coordinate distance between adjacent type points is less than a preset threshold (i.e., the preset coordinate distance), it is considered that the two lane line segments actually belong to the same lane or are very close to each other, and they can be regarded as different parts of the same lane. At this time, the system will logically merge the current frame segmented lines to which they belong based on these midpoint type points to generate a continuous, longer lane line, i.e., the merged lane line.

[0167] In an embodiment of the present invention, the above steps help eliminate lane line fragmentation caused by errors in the data collection or processing process, making the constructed lane-level local road map more coherent and accurate, and providing clearer and more consistent navigation guidance for autonomous vehicles.

[0168] Optionally, rendering lane-level navigation guidance segments in the lane-level local road map based on the precise map data, the navigation route information, and the current lane information to update the lane-level local road map includes the following steps:

[0169] Determine the path to be traveled based on accurate map data, navigation route information, current lane information, and merging lane lines;

[0170] Determining a target lane line constituting a path to be traveled based on the merged lane line;

[0171] Determine the coordinate information of the lane-level navigation guidance point based on the coordinate information of the midpoint lane line point corresponding to the target lane line;

[0172] Generate lane-level navigation guidance line segments based on the coordinate information of the lane-level navigation guidance points;

[0173] determining a lane boundary line based on the second lane line;

[0174] Rendering lane-level navigation guidance line segments and lane boundary lines in the lane-level local road map to update the lane-level local road map.

[0175] In an embodiment of the present invention, determining the path to be traveled based on standard precision map data, navigation route information, current lane information and merged lane lines can be understood as, combining standard precision (standard precision) map data, route information planned by the vehicle navigation system, lane information in which the vehicle is currently located, and merged lane lines obtained through the above process, the system can intelligently analyze and determine the travel path that the vehicle should follow.

[0176] Determining the target lanes that make up the planned route based on the merged lanes can be understood as taking the planned route and then identifying the specific merged lanes that make up that route, thus obtaining the target lanes. This selection of target lanes ensures that the planned route matches real-time road conditions, providing clear driving guidance for the vehicle.

[0177] Determining the coordinate information of the lane-level navigation guidance point based on the coordinate information of the midpoint lane line type point corresponding to the target lane line can be understood as selecting the midpoint lane line type point on the target lane line as the key reference point, and using the coordinate information of these type points to determine a series of navigation guidance point coordinates.

[0178] Generating lane-level navigation guidance lines based on the coordinates of lane-level navigation guidance points can be understood as connecting the previously determined lane-level navigation guidance points to form continuous line segments, known as lane-level navigation guidance lines. These guidance lines intuitively display the vehicle's intended driving direction. Through dynamic updates and real-time rendering, they provide clear visual guidance to the driver or the autonomous driving system, helping the vehicle safely and accurately navigate the target lane.

[0179] Determining lane boundaries based on secondary lane lines can be understood as using previously classified secondary lane lines (i.e., lane lines where the left and right lanes have different driving directions) to identify and mark lane boundaries—lines that the vehicle should avoid crossing. Lane boundary recognition helps the automated driving system maintain the vehicle's correct lane and avoid accidents caused by straying into other traffic streams.

[0180] Rendering lane-level navigation guidance lines and lane boundaries within the lane-level local road map to update the map can be understood as adding the generated lane-level navigation guidance lines and determined lane boundaries to the lane-level local road map for visual display. By updating these key map elements in real time, the system can provide the driver or autonomous driving system with the latest and most detailed lane-level navigation information, ensuring safety and accuracy during driving.

[0181] In an embodiment of the present invention, the above steps combine high-precision maps, real-time navigation routes, current lane information, and optimized lane line data to accurately calculate the vehicle's intended path, determine the target lane line on the path, and generate lane-level navigation guidance using midpoint lane line points to ensure the vehicle follows the correct lane. At the same time, lane boundaries are clearly defined to improve driving safety. By rendering navigation guidance and lane boundaries in real time within a local map, the present invention implements lane-level navigation services without the need for high-precision maps, significantly reducing application costs, improving the flexibility and reliability of the autonomous driving system, and providing an efficient and safe solution for smart travel.

[0182] Figure 8 This is a schematic diagram of sorting, merging and filtering local lane lines within a grid according to an embodiment of the present invention. As shown in the figure, exemplarily, based on the x-coordinate and y-coordinate of the midpoint of the local lane lines, the lane lines within each grid are sorted, and the double pointer traverses the lane lines within each grid. If the x-coordinates or y-coordinates of the midpoints of two lane lines are similar, the two can be merged. If the types do not match, the solid line is mainly used, and the merging rules are solid line + solid line = solid line, solid line + dotted line = solid line, dotted line + dotted line = dotted line. The local lane lines are filtered, including removing the lane lines to the left of the left boundary of the drivable area of ​​the vehicle and to the right of the right boundary of the drivable area of ​​the vehicle in each grid. After merging and filtering, the local lane line data within the grid can describe the lane-level mini-map situation ahead, which is used for subsequent path planning and is not limited here.

[0183] This application defines three classes in code language for building and managing lane-level mini-map data structures. These classes are designed to store and process information related to road boundaries, lane lines, and entire road segments (segments), thereby supporting lane-level navigation and autonomous driving functions. The first is the SRLocalBoundary structure. The SRLocalBoundary structure is a data structure used in the present invention to describe the characteristics of lane boundaries or lane lines in local road maps. The type in the SRLocalBoundary structure represents the boundary type, which may be the type of lane line (such as solid line, dotted line, etc.). startPoint is the two-dimensional coordinate of the starting point of the lane line, and the Vector2 type is usually used to represent the position of a point (x, y). endPoint is the two-dimensional coordinate of the end point of the lane line, and the Vector2 type is also used. midPoint is the two-dimensional coordinate of the midpoint of the lane line, which is used to provide midpoint position information when processing lane line segments. featurePointNum may represent the number of feature points on the boundary or lane line, which is used for retrieval or processing of lane line features in subsequent algorithms. The second is the SRLocalSegment class. The SRLocalSegment class is a data structure with lane-level details representing a section of the road in front of or on the vehicle in this invention. The boundaries in the SRLocalSegment class is a List <srlocalboundary>A list of type, used to store multiple lane boundaries or lane line information within a road segment (segment). startDist and endDist represent the starting distance and ending distance of the road segment respectively, and the unit may be meters, which is used to describe the position of the road segment in the direction of vehicle travel. curLaneNum is the number of lanes in the current road segment, which is used to assist vehicle positioning and path planning. The Clean method is used to clear the data in the SRLocalSegment object, including clearing the boundary list and resetting the distance and number of lanes parameters to the default values. This is very useful when updating minimap data to ensure that the system can rebuild the data from a clean state. Finally, there is the SRLocalSegmentInfo class. The SRLocalSegmentInfo class is a container for managing and storing multiple SRLocalSegment instances in the present invention. The SRLocalSegmentInfo class integrates the lane-level information of all road segments as part of the lane-level local road map, where segments in the SRLocalSegmentInfo class is a List <srlocalsegment>A list of this type is used to store information about multiple road segments for the entire minimap. validSegNum represents the number of valid road segments, that is, the number of road segments actually used in the current minimap. This helps the algorithm quickly locate and process valid data. The Clean method is used to clear the data in the SRLocalSegmentInfo object. First, the number of valid road segments is reset to 0. Then, the segments list is iterated, calling the Clean method of each SRLocalSegment object to clear the entire minimap data to zero, ensuring real-time and accurate data.

[0184] In summary, this application constructs a lane-level mini-map data model by defining three classes: SRLocalBoundary, SRLocalSegment, and SRLocalSegmentInfo. The model includes the lane line type, location information (starting point, end point, midpoint), the number of feature points on the lane line, as well as key attributes such as the start and end distances of the road segment and the number of lanes. Through these data structures, the system can efficiently store and process the information required for lane-level navigation. At the same time, the implementation of the Clean method ensures timely updating and cleaning of data, providing a solid data foundation for real-time navigation and autonomous driving functions.

[0185] Based on perception data and navigation v2 data, this application can construct lane-level mini-map information from the front of the vehicle, and can simplify the road topology structure through gridding to construct lane-level mini-map information from the front of the vehicle.

[0186] In addition, this application relies heavily on autonomous driving perception capabilities, and the perception data used include lane line points, lane type attributes, and left and right driving directions of lane lines. If there is further lane line topology information, such as left-right relationships, connectivity relationships, etc., this application can further construct a more accurate road grid. This application can also be based on downstream algorithm requirements, and the mapping module needs to provide some Application Programming Interface (API) query interfaces. For example, obtaining the lane index where the vehicle is located, obtaining the number of lanes at the corresponding position given a given front distance, and other information are not restricted here.

[0187] 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0188] According to an embodiment of the present invention, an embodiment of a navigation device for a lane-level local map is provided. It should be noted that the device can be used to execute the above-mentioned road network mapping method.

[0189] Figure 9 is a schematic diagram of a navigation device based on a lane-level local map according to an embodiment of the present invention. Figure 9 As shown, the navigation device 900 of the lane-level local map includes: an activation module 901 for activating the local map navigation mode in response to receiving a local map navigation activation signal; an acquisition module 902 for acquiring precise map data and perception data, wherein the precise map data is used to provide road network structure data of the roads around the vehicle, and the perception data is used to describe the road environment of the roads around the vehicle; a construction module 903 for constructing a lane-level local road map based on the precise map data and the perception data, wherein the lane-level local road map is used to display the roads within a first preset distance range in front of the vehicle that match the navigation route, and the lane-level local road map is used to display the roads within a first preset distance range in front of the vehicle that match the navigation route. The local road map includes lane lines; a rendering module 904 is used to render lane-level navigation guide line segments in the lane-level local road map based on the precision map data, navigation route information and the current lane information of the vehicle, wherein the lane-level navigation guide line segments are located within a first preset distance range in front of the vehicle in the navigation interface, the current lane information of the vehicle is the lane information of the vehicle currently located in the lane-level local road map, and the lane-level navigation guide line segments are used to provide navigation information of the vehicle in the lane-level local road map; a navigation module 905 is used to navigate the vehicle based on the lane-level local road map and the lane-level navigation guide line segments.

[0190] According to another aspect of the embodiments of the present invention, a vehicle is provided for executing the methods in various embodiments of the present invention.

[0191] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0192] According to another aspect of an embodiment of the present invention, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention when running.

[0193] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0194] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0196] The units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the present embodiment.

[0197] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0198] If the integrated unit is implemented in the form of 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 the present invention, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0199] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.< / srlocalsegment> < / srlocalboundary>

Claims

1. A lane-level local map navigation method, applied to a vehicle, characterized in that: include: In response to receiving a local map navigation start signal, starting a local map navigation mode; Acquiring precise map data and perception data, wherein the precise map data is used to provide road network structure data of roads around the vehicle, and the perception data is used to describe the road environment of the roads around the vehicle; constructing a lane-level local road map based on the precise map data and the perception data, wherein the lane-level local road map is used to display roads within a first preset distance range ahead of the vehicle that match the navigation route, and the lane-level local road map includes lane markings; Rendering a lane-level navigation guide line segment in the lane-level local road map based on the precise map data, the navigation route information, and the current lane information of the vehicle, wherein the lane-level navigation guide line segment is located within the first preset distance range in front of the vehicle in the navigation interface, the current lane information of the vehicle is the lane information of the vehicle in the lane-level local road map, and the lane-level navigation guide line segment is used to provide navigation information for the vehicle in the lane-level local road map; The vehicle is navigated based on the lane-level local road map and the lane-level navigation guidance line segments.

2. The method according to claim 1, characterized in that The lane-level local road map is further used to display roads within a second preset distance range behind the vehicle, where the area of ​​the second preset distance range is smaller than the area of ​​the first preset distance range.

3. The method according to claim 1, characterized in that The starting point of the lane-level navigation guidance line segment is located at the front position of the vehicle in the navigation interface, and the end point of the lane-level navigation guidance line segment is located on the road within the first preset distance range in front of the vehicle that matches the navigation route in the navigation interface. The display area of ​​the lane-level local road map is smaller than the display area of ​​the vehicle navigation interface.

4. The method according to any one of claims 1 to 3, characterized in that The constructing of a lane-level local road map based on the precise map data and the perception data includes: Determine a plurality of grids in the vehicle coordinate system according to the standard map data; Determine a lane line point corresponding to the current frame according to the perception data; Mapping the lane line point of the current frame to the multiple grids in the vehicle coordinate system to obtain a mapping relationship between the lane line point of the current frame and the multiple grids, wherein the mapping relationship is used to record the grid to which any lane line point of the current frame belongs; In any grid, combining the lane line points of the current frame in the any grid according to the mapping relationship to obtain the segmented line of the current frame in the any grid; A lane-level local road map is constructed based on the current frame segmentation line.

5. The method according to claim 4, characterized in that Determining the plurality of grids in the vehicle coordinate system according to the precise map data includes: Determining a grid division range of the vehicle coordinate system based on the precise map data, wherein the grid division range is from a second preset distance behind the vehicle in the direction of travel to a first fork in the road or a third preset distance in the direction of travel of the vehicle; Determining a location to be divided based on a preset division interval and a road segment dividing point within the grid division range; The vehicle coordinate system is divided based on the positions to be divided to obtain the multiple grids in the vehicle coordinate system.

6. The method according to claim 5, characterized in that The determining of the to-be-divided position based on the preset division interval and the road segment dividing point in the grid division range includes: In response to the presence of a road segment dividing point in the grid division range, determining a first division position within a first range based on the preset division interval, wherein the first range is from the second preset distance behind the vehicle in the driving direction to the road segment dividing point; Determining a second division position based on the preset division interval within a second range, wherein the second range is from the road segment dividing point to the first fork in the vehicle's travel direction or the third preset distance; The to-be-divided position is determined based on the first dividing position and the second dividing position.

7. The method according to claim 5, characterized in that The determining of the to-be-divided position based on the preset division interval and the road segment dividing point in the grid division range includes: In response to the presence of a plurality of the road segment dividing points in the grid division range, determining a third division position within a third range based on the preset division interval, wherein the plurality of road segment dividing points include a first road segment dividing point and a second road segment dividing point, the first road segment dividing point is closer to the vehicle than the second road segment dividing point, and the third range is from the second preset distance behind the vehicle in the direction of travel to the first road segment dividing point; Determining a fourth dividing position based on the preset dividing interval within a fourth range, wherein the fourth range is from the first road section dividing point to the second road section dividing point; Determining a fifth division position based on the preset division interval within a fifth range, wherein the fifth range is from the second road segment dividing point to the first fork in the road ahead of the vehicle or the third preset distance; The to-be-divided position is determined based on the third divided position, the fourth divided position, and the fifth divided position.

8. The method according to claim 4, characterized in that In any grid, combining the lane line points of the current frame in any grid according to the mapping relationship to obtain the segmented line of the current frame in any grid includes: determining lane markings in a road around the vehicle based on the perception data; Classifying the lane lines based on a lane line classification rule to obtain a first lane line and a second lane line, wherein a left lane driving direction and a right lane driving direction of the first lane line are consistent, and a left lane driving direction and a right lane driving direction of the second lane line are inconsistent; Determine a plurality of target lane line-type points in any one of the grids based on the lane line-type points of the first lane line; The multiple target lane line points are combined to obtain a current frame segmentation line in any one of the grids.

9. The method according to claim 8, characterized in that The constructing of a lane-level local road map based on the current frame segmentation line includes: Determine the midpoint lane line point of the current frame segment line; Merging the segmented lines of the current frame based on the coordinate information of the midpoint lane line point to obtain a merged lane line; Adjusting the lane line type of the merged lane line based on a lane line type rule to obtain the adjusted merged lane line, wherein the lane line type rule is used to merge lane lines with mismatched lane line types into a solid line type lane line; The lane-level local road map is constructed based on the adjusted current frame segmentation line.

10. The method according to claim 9, characterized in that The merging of the current frame segmented lines based on the coordinate information of the midpoint lane line point to obtain a merged lane line includes: In response to a coordinate distance between adjacent midpoint lane line-type points being less than a preset coordinate distance, the current frame segment lines to which the adjacent midpoint lane line-type points belong are merged to obtain the merged lane line.

11. The method according to claim 9, characterized in that The rendering of lane-level navigation guidance line segments in the lane-level local road map based on the precise map data, the navigation route information, and the current lane information to update the lane-level local road map includes: Determine a path to be traveled based on the precise map data, the navigation route information, the current lane information, and the merged lane line; Determining a target lane line constituting the path to be traveled based on the merged lane line; Determine the coordinate information of the lane-level navigation guidance point based on the coordinate information of the midpoint lane line point corresponding to the target lane line; generating the lane-level navigation guidance line segment based on the coordinate information of the lane-level navigation guidance point; determining a lane boundary line based on the second lane line; The lane-level navigation guidance line segment and the lane boundary line are rendered in the lane-level local road map to update the lane-level local road map.

12. A lane-level local map navigation device, applied to a vehicle, characterized in that: include: an activation module, configured to activate a local map navigation mode in response to receiving a local map navigation activation signal; an acquisition module, configured to acquire precise map data and perception data, wherein the precise map data is used to provide road network structure data of roads surrounding the vehicle, and the perception data is used to describe the road environment of the roads surrounding the vehicle; a construction module, configured to construct a lane-level local road map based on the precise map data and the perception data, wherein the lane-level local road map is configured to display roads within a first preset distance range ahead of the vehicle that match the navigation route, and the lane-level local road map includes lane markings; a rendering module, configured to render a lane-level navigation guide line segment in the lane-level local road map based on the precise map data, the navigation route information, and the current lane information of the vehicle, wherein the lane-level navigation guide line segment is located within the first preset distance range in front of the vehicle in the navigation interface, the current lane information of the vehicle is the lane information of the vehicle in the lane-level local road map, and the lane-level navigation guide line segment is used to provide navigation information of the vehicle in the lane-level local road map; A navigation module is used to navigate the vehicle based on the lane-level local road map and the lane-level navigation guidance line segments.

13. A vehicle, characterized in that: The vehicle is used to execute the lane-level local map navigation method described in any one of claims 1 to 11 above.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the lane-level local map navigation method described in any one of claims 1 to 11 when running on a computer or a processor.

15. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the lane-level local map navigation method described in any one of claims 1 to 11.

16. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the lane-level local map navigation method as claimed in any one of claims 1 to 11.

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