High-precision Map Generation Method, System and Device Based on Driving Route

Abstract

The present invention relates to the field of navigation technology, and particularly relates to a high-precision map generation method, system and device based on a driving route. The method includes: obtaining several types of traffic information corresponding to the current intersection in the vehicle driving route; analyzing the real-time influence of the traffic information corresponding to the current intersection on route planning according to each type of traffic information corresponding to the current intersection, and determining the real-time information influence coefficient of the current intersection; analyzing the influence degree of each type of traffic information on the route direction according to the real-time information influence coefficient, and determining the route direction coefficient of each type of traffic information corresponding to the current intersection; determining the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection according to the route direction coefficient. Through the present invention, a suitable display method can be determined for each type of traffic information corresponding to the current intersection on the high-precision map passed by during driving, thereby meeting the driver's demand for accurate judgment of the driving route direction.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and particularly to a high-precision map generation method, system and device based on a driving route. Background Art

[0002] High-precision maps play an important role in autonomous driving and intelligent navigation, and can provide precise information such as lane lines, traffic signs, traffic lights, intersection layouts, etc. When generating high-precision maps, there are differences in the actual traffic information of different intersections, and there are different requirements for the display fineness of traffic information for each intersection. Specifically, it refers to the display fineness of each type of information (such as lane lines, traffic signs, traffic lights, etc.) at each intersection in the high-precision map in the image.

[0003] The display of different types of traffic information on existing high-precision maps usually adopts a fixed information display method, which is mainly for static environments. In an environment with complex dynamic information updates (such as temporary traffic signs, construction areas), the existing fixed information display method is difficult to meet the driver's need for accurate judgment of the driving route direction, thereby affecting the navigation accuracy of high-precision maps in complex road conditions. Summary of the Invention

[0004] In order to solve the technical problem that the display method of different types of traffic information on the existing high-precision map is difficult to meet the driver's need for accurate judgment of the driving route direction, the purpose of the present invention is to provide a high-precision map generation method, system and device based on a driving route, and the specific technical solutions adopted are as follows:

[0005] An embodiment of the present invention provides a high-precision map generation method based on a driving route, and the method includes:

[0006] Obtain several types of traffic information corresponding to the current intersection in the vehicle driving route, where the traffic information includes static traffic information and dynamic traffic information;

[0007] Analyze the real-time influence of the traffic information of the current intersection on the path planning according to each type of traffic information corresponding to the current intersection, and determine the real-time information influence coefficient of the current intersection;

[0008] Analyze the influence degree of each type of traffic information on the path direction according to the real-time information influence coefficient of the current intersection, and determine the path direction coefficient of each type of traffic information corresponding to the current intersection;

[0009] Determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information of the current intersection according to the path direction coefficient of each type of traffic information corresponding to the current intersection.

[0010] Further, analyzing the real-time impact of the traffic information at the current intersection on path planning according to each type of traffic information corresponding to the current intersection, and determining the real-time information impact coefficient of the current intersection, including:

[0011] For the current intersection, determining the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information at the current moment compared with the previous moment;

[0012] Taking the current intersection as a node and the path as a connection between nodes, obtaining the number of connections of the node corresponding to the current intersection;

[0013] According to the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information, and combining the number of connections of the node corresponding to the current intersection, determining the real-time information impact coefficient of the current intersection.

[0014] Further, the determination method of the number of type changes includes:

[0015] Obtaining each type of pending traffic information at the current moment and each type of pending traffic information at the previous moment of the current moment, and compared with each type of pending traffic information at the previous moment of the current moment, counting the number of new types and the number of disappeared types;

[0016] Taking the value obtained by adding the number of new types and the number of disappeared types as the number of type changes corresponding to the pending traffic information, and the pending traffic information is static traffic information or dynamic traffic information.

[0017] Further, according to the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information, and combining the number of connections of the node corresponding to the current intersection, determining the real-time information impact coefficient of the current intersection, including:

[0018] Analyzing the relative relationship between the static and dynamic changes according to the number of type changes corresponding to the static and dynamic traffic information, and obtaining the initial information coefficient of the current intersection;

[0019] Taking the number of connections of the node corresponding to the current intersection as the weight of the initial information coefficient, and performing weighted processing on the initial information coefficient to obtain the real-time information impact coefficient of the current intersection.

[0020] Further, analyzing the influence degree of each type of traffic information on the path direction according to the real-time information impact coefficient of the current intersection, and determining the path direction coefficient of each type of traffic information corresponding to the current intersection, including:

[0021] Obtain the data update frequency of each type of traffic information at the current intersection in each connection direction, and then calculate the average value of the data update frequency of each type of traffic information at the current intersection in all connection directions;

[0022] According to the data update frequency of each type of traffic information at the current intersection in each connection direction and the average value of the data update frequency, determine the information change proportion of each type of traffic information in multiple connection directions at the current intersection;

[0023] According to the information change proportion and the real-time information influence coefficient of the current intersection, determine the path direction coefficient of each type of traffic information corresponding to the current intersection.

[0024] Further, the determining the path direction coefficient of each type of traffic information corresponding to the current intersection includes:

[0025] For any type of traffic information, obtain the fusion value of the information change proportion and the real-time information influence coefficient of the current intersection;

[0026] Perform normalization processing on the fusion value to obtain the first normalized value, and use the first normalized value as the path direction coefficient of the corresponding type of traffic information.

[0027] Further, the determining the display fineness of the road condition information on the graphic area corresponding to each type of traffic information of the current intersection includes:

[0028] Obtain the graphic area corresponding to each type of traffic information of the current intersection; for any type of traffic information, regard the other types of traffic information that overlap with the graphic area of this type of traffic information as the relevant type of traffic information;

[0029] According to the path direction coefficients of each relevant type of traffic information corresponding to each type of traffic information, and the overlapping area between the graphic area corresponding to each type of traffic information and the graphic areas of its respective relevant types of traffic information, determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information of the current intersection.

[0030] Further, the determining the display fineness of the road condition information on the graphic area corresponding to each type of traffic information of the current intersection includes:

[0031] For any relevant type of traffic information corresponding to any type of traffic information, determine the ratio of the path direction coefficient of this relevant type of traffic information of this type of traffic information to the normalized value of the overlapping area;

[0032] For each of the relevant types of traffic information corresponding to this type of traffic information, calculate the average value of all the ratios corresponding to this type of traffic information to obtain the average ratio;

[0033] Perform normalization processing on the average ratio to obtain a second normalized value, and use the second normalized value as the display fineness of the road condition information on the graphic area corresponding to this type of traffic information at the current intersection.

[0034] Another embodiment of the present invention provides a high-precision map generation system based on a driving path. The system includes:

[0035] A traffic information collection module, configured to obtain several types of traffic information corresponding to the current intersection in the vehicle driving path, where the traffic information includes static traffic information and dynamic traffic information;

[0036] An information coefficient determination module, configured to analyze the real-time influence status of the traffic information at the current intersection on the path planning according to each type of traffic information corresponding to the current intersection, and determine the real-time information influence coefficient of the current intersection;

[0037] A direction coefficient determination module, configured to analyze the influence degree of each type of traffic information on the path direction according to the real-time information influence coefficient of the current intersection, and determine the path direction coefficient of each type of traffic information corresponding to the current intersection;

[0038] A fineness determination module, configured to determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection according to the path direction coefficient of each type of traffic information corresponding to the current intersection.

[0039] Another embodiment of the present invention provides a high-precision map generation device based on a driving path, including a processor and a memory. The processor is configured to process instructions stored in the memory to implement a high-precision map generation method based on a driving path.

[0040] The present invention has the following beneficial effects:

[0041] The present invention provides a method, system and device for generating a high-precision map based on a driving route. The method determines the real-time information influence coefficient of the current intersection by obtaining several types of traffic information corresponding to the current intersection in the vehicle driving route, can update static and dynamic road condition information in real time, and achieves the effect of improving driving safety, enabling the driver to identify potential risks in a timely manner; analyzes the influence degree of each type of traffic information on the route direction according to the real-time information influence coefficient, determines the route direction coefficient of each type of traffic information corresponding to the current intersection, and determining the route direction coefficient can provide clear and accurate navigation instructions at complex intersections, achieving the effect of enhancing navigation accuracy and reducing misguidance and driving errors; determines the display fineness of the road condition information on the graphic area corresponding to each type of traffic information of the current intersection according to the route direction coefficient, and determining the display fineness is also the display method of different types of traffic information obtained by integrating multiple data sources, which helps to achieve the effect of enhancing information integration and improves the comprehensiveness and accuracy of information. In short, through the present invention, the intelligence and safety of the overall traffic system can be jointly promoted, meeting the driver's demand for accurate judgment of the driving route direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other accompanying drawings without creative efforts based on these drawings.

[0043] Figure 1 It is a flowchart of a method for generating a high-precision map based on a driving route provided by an embodiment of the present invention;

[0044] Figure 2 It is a flowchart for implementing step S2 in an embodiment of the present invention;

[0045] Figure 3 It is a flowchart for implementing step S3 in an embodiment of the present invention;

[0046] Figure 4 It is a flowchart for implementing step S4 in an embodiment of the present invention;

[0047] Figure 5 It is a structural diagram of a system for generating a high-precision map based on a driving route provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the specific implementation manners, structures, features and effects of the technical solutions proposed according to the present invention will be described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0050] An embodiment of the present invention provides a method for generating a high-precision map based on a driving route, as Figure 1 shown, including the following steps:

[0051] S1, obtaining several types of traffic information corresponding to the current intersection in the vehicle driving route.

[0052] The traffic information includes static traffic information and dynamic traffic information.

[0053] The core purpose of a high-precision map is to provide real-time and accurate environmental information to support driving and intelligent transportation applications. Therefore, the content displayed on a high-precision map is mainly divided into a static part and a dynamic part. The static part includes fixed information such as road layouts and traffic signs, which are usually obtained through traditional map data and high-precision sensors, and the update cycle is relatively long; while the dynamic part includes real-time road conditions, traffic events and environmental perception information, which are mainly obtained in real time through in-vehicle sensors, vehicle networks and cloud data, and changes relatively quickly, having a more direct impact on the driving route. In the process of generating a high-precision map, the static part and the dynamic part complement each other. The static part provides a stable geographical basis, while the dynamic part helps the system quickly respond to environmental changes through real-time feedback to ensure driving safety and efficiency.

[0054] Specifically, data collection is performed on the driving route of a real-time vehicle. The edge device of the vehicle realizes real-time data collection through multiple sensors (such as lidar, high-definition cameras, radars and ultrasonic sensors) to obtain several types of traffic information corresponding to the current intersection.

[0055] Different types of traffic information play different roles in navigation. For example, lidar emits laser beams and receives reflected waves to generate high-precision three-dimensional point cloud data, depicting the structure of the surrounding environment in real time; high-definition cameras capture environmental images and use computer vision technology to identify traffic signs, signal lights, pedestrians, and other vehicles; radar sensors emit electromagnetic waves to monitor the speed and distance of dynamic objects, ensuring that the vehicle can accurately judge the motion state of surrounding objects; while ultrasonic sensors are mainly used for short-range detection to help the vehicle identify surrounding obstacles when driving at low speeds. The types and numbers of traffic information collected at different times may be in a changing state. Therefore, the types and numbers of traffic information collected for static and dynamic traffic information are obtained by the implementer according to the specific real-time situation, and no specific limitation is provided here.

[0056] So far, this embodiment has obtained each type of traffic information corresponding to the current intersection in the vehicle driving path.

[0057] The static-dynamic difference significantly affects path planning and requirements in the application of high-precision maps, mainly reflected in data characteristics, update frequency, and the ability to respond to environmental changes. The static part usually includes relatively fixed information such as road structure, number of lanes, traffic signs, and intersection layout, with high stability. This indicates that in path planning, the static part provides basic navigation information to ensure accurate navigation of the vehicle on the established route. Relatively speaking, the dynamic part reflects real-time changing information, such as traffic flow, weather conditions, obstacles, and accidents. The dynamic changes are frequent and have great uncertainty, and they have a more obvious impact on the vehicle's driving path.

[0058] Therefore, the map accuracy generation requirements for real-time vehicle path planning are easily affected by the display fineness of different types of traffic information during navigation. The analysis of the display fineness can be achieved through steps S2 to S4.

[0059] S2. Analyze the real-time impact of the traffic information at the current intersection on path planning according to each type of traffic information corresponding to the current intersection, and determine the real-time information impact coefficient of the current intersection.

[0060] For the information requirements of high-precision map generation, the effective planning of its path is determined by the real-time impact of different types of static and dynamic road conditions information extracted when passing through the current intersection in the real-time vehicle driving environment, that is, the degree of dynamic random impact on the current intersection, that is, the relative relationship between static and dynamic changes. The relative relationship refers to whether the information update at the current intersection is more affected by dynamic type traffic information or static type traffic information.

[0061] The above step S2 can be achieved through Figure 2 the steps S21 to S23 shown below:

[0062] S21. For the current intersection, determine the number of type changes in the static traffic information and the number of type changes in the dynamic traffic information at the current moment compared to the previous moment.

[0063] In this embodiment, the method for determining the number of type changes in the static traffic information and the dynamic traffic information is the same. The static traffic information and the dynamic traffic information are collectively referred to as the to-be-determined traffic information to determine the number of type changes in the to-be-determined traffic information.

[0064] Specifically, obtain each type of to-be-determined traffic information at the current moment and each type of to-be-determined traffic information at the previous moment of the current moment; compared with each type of to-be-determined traffic information at the previous moment of the current moment, count the number of newly added types and the number of disappeared types; use the value obtained by adding the number of newly added types and the number of disappeared types as the number of type changes in the to-be-determined traffic information, where the to-be-determined traffic information is static traffic information or dynamic traffic information.

[0065] For example, if the static traffic information at the current moment is A, B, and C respectively, and the static traffic information at the previous moment of the current moment is B, C, and D respectively, then the number of newly added types of static traffic information is 1, the number of disappeared types is 1, and the number of type changes in the static traffic information is 2.

[0066] S22. Determine the current intersection as a node and the path as the connection between nodes to obtain the number of connections of the node corresponding to the current intersection.

[0067] In this embodiment, in order to reflect the road structure change, each current intersection of the driving paths on the navigation path can be quantified into a node, and the path is quantified as the connection between nodes. This connection is realized based on the possible path planning between the current position of the vehicle and the destination, that is, there is a sequential arrival relationship between the current intersection and the next intersection for multiple existing paths that can reach the destination, and each node has at least two connections. For any node, a connection can be obtained by connecting the node with its previous node, and a connection can be obtained by connecting the node with its next node. Therefore, each node has at least two connections.

[0068] S23. According to the number of type changes in the static traffic information and the number of type changes in the dynamic traffic information, combined with the number of connections of the node corresponding to the current intersection, determine the real-time information influence coefficient of the current intersection.

[0069] In this embodiment, the real-time information influence coefficient refers to the degree of influence of real-time static and dynamic traffic information on the path planning of the current intersection. Analyzing the relative relationship between static and dynamic changes can quantify the degree of influence of real-time traffic information on path planning; the number of connection lines of the nodes corresponding to the current intersection can analyze the complexity of the path planning of the current intersection. The more connection lines there are, the higher the complexity of the path planning, and the greater the influence of real-time traffic information on path planning.

[0070] Specifically, according to the number of type changes corresponding to static and dynamic traffic information, analyze the relative relationship between static and dynamic changes to obtain the initial information coefficient of the current intersection; use the number of connection lines of the nodes corresponding to the current intersection as the weight of the initial information coefficient, and perform weighted processing on the initial information coefficient to obtain the real-time information influence coefficient of the current intersection.

[0071] As an example, the calculation formula for the real-time information influence coefficient of the current intersection can be:

[0072] ; where, represents the real-time information influence coefficient of the current intersection, i represents the current intersection, norm represents the linear normalization function, which is used to limit the value range of the data in the brackets between 0 and 1, represents the number of type changes corresponding to the dynamic traffic information of the current intersection, represents the number of type changes corresponding to the static traffic information of the current intersection, c represents a non-zero constant, which is used to avoid the possibility of the denominator of the fraction being zero, and the empirical value is taken as 0.01, represents the initial information coefficient of the current intersection, represents the number of connection lines of the nodes corresponding to the current intersection, and also represents the weight of the initial information coefficient.

[0073] In the calculation formula of the real-time information influence coefficient, represents the degree of influence of the change of the real-time information type at the location of the current intersection, The larger it is, the greater the influence of the traffic information of the current intersection on the nearby route planning, and the larger the initial information coefficient; the number of connection lines The larger it is, the more complex the road conditions of the current intersection are, and the higher the importance of the traffic information of the current intersection to the nearby route planning, that is, the greater the weight of the initial information coefficient.

[0074] The dynamic traffic information has a major impact on the route planning of the current intersection. Therefore, the dynamic traffic information is placed in the denominator position of the fraction. However, the static traffic information also affects the route planning. Therefore, when determining the initial information coefficient, the number of type changes of dynamic and static traffic information needs to be combined for analysis. Compared with only analyzing the number of type changes of dynamic and static traffic information, when calculating the real-time information influence coefficient, combining the number of connection lines of the corresponding node of the current intersection can analyze the complexity of the route planning of the current intersection. The more connection lines there are, the greater the complexity of the route planning of the current intersection and the greater the impact of the real-time traffic information of the current intersection. This can effectively improve the numerical accuracy of the real-time information influence coefficient.

[0075] So far, this embodiment has obtained the real-time information influence coefficient of the current intersection.

[0076] S3. Analyze the influence degree of each type of traffic information on the path direction according to the real-time information influence coefficient of the current intersection, and determine the path direction coefficient of each type of traffic information corresponding to the current intersection.

[0077] The driving path indicates an information requirement. Especially, the different amounts of information obtained on the path lead to changes in the road condition complexity, which affect the accuracy requirement of the high-precision map and the path display length of the high-precision map. This is mainly reflected in the increase in the type and quantity of parts such as vehicle-pedestrian intersections. Therefore, each type of traffic information has a certain path directionality, that is, to determine the path direction coefficient, and the path direction coefficient refers to the influence degree of each type of traffic information on the path direction.

[0078] In this embodiment, the navigation path of real-time vehicles mainly depends on the connection characteristics between nodes, that is, the information pointing relationship of different types of traffic information on the intersection to different driving paths. When shown between nodes, it is the pointing relationship of the connection lines between nodes. For any type of traffic information, the greater the proportion of information changes on multiple connection lines of the current intersection, the more obvious the path directionality of this type of traffic information.

[0079] The path direction coefficient of the j-th type of traffic information extracted from the corresponding node of the current intersection is determined through Figure 3 the steps S31 to S33 shown as follows:

[0080] S31. Obtain the data update frequency of each type of traffic information at the current intersection in each connection line direction, and then calculate the average value of the data update frequency of each type of traffic information at the current intersection in all connection line directions.

[0081] In this embodiment, the data update frequency is affected by the change in the data acquisition frequency implemented by the edge sensor. The greater the data update frequency, the greater the proportion of information change in the corresponding type of traffic information, and the more obvious the path directionality. The average value of the data update frequency is used for comparative analysis with the change in the data acquisition frequency, which refers to the average value calculated by averaging the data update frequencies of the same type of traffic information in all connection directions. The connection direction refers to the connection relationship between nodes, and the connection corresponding to the node is bidirectional. Since the driving route is the location of the vehicle, only the path associated with the node needs to be analyzed for directionality.

[0082] S32. Determine the proportion of information change of each type of traffic information in multiple connection directions at the current intersection according to the data update frequency and the average value of the data update frequency of each type of traffic information in each connection direction at the current intersection.

[0083] As an example, the calculation formula for the proportion of information change of the j-th type of traffic information in multiple connection directions at the current intersection can be:

[0084] ; In the formula, represents the proportion of information change of the j-th type of traffic information in multiple connection directions at the current intersection, K represents the number of connection directions, k represents the k-th connection direction, represents the data update frequency of the j-th type of traffic information in the k-th connection direction, represents the average value of the data update frequencies of the j-th type of traffic information in all connection directions.

[0085] In the calculation formula of the proportion of information change, represents the degree of difference between the data update frequency of the j-th type of traffic information in the k-th connection direction and the average value of the data update frequency. The larger it is, the greater the impact of the j-th type of traffic information on the path planning in the k-th connection direction, and the stronger the path directionality; represents the degree of difference with respect to the difference situation of the average value of the data update frequency. is used as a reference value in the fraction to compare the degree of difference , The larger it is, the larger the proportion of information change of the j-th type of traffic information on the k-th connection at the current intersection. By accumulating the proportion of information change of the j-th type of traffic information on all connections at the current intersection, the proportion of information change of the j-th type of traffic information in multiple connection directions at the current intersection can be obtained. The proportion of information change is one of the key calculation factors for determining the path direction coefficient, and each type of traffic information has its corresponding proportion of information change.

[0086] S33. Determine the path direction coefficient of each type of traffic information corresponding to the current intersection according to the information change ratio and the real-time information influence coefficient of the current intersection.

[0087] Specifically, for any type of traffic information, obtain the fusion value of the information change ratio and the real-time information influence coefficient of the current intersection; perform normalization processing on the fusion value to obtain the first normalized value, and use the first normalized value as the path direction coefficient of the corresponding type of traffic information.

[0088] In this embodiment, for the j-th type of traffic information, calculate the product of the information change ratio and the real-time information influence coefficient; perform normalization processing on the product by using a linear normalization function to obtain a normalized value; use the obtained normalized value as the path direction coefficient of the j-th type of traffic information corresponding to the current intersection.

[0089] In the calculation process of the path direction coefficient, the real-time information influence coefficient is equivalent to the confidence level of the information change ratio of the current intersection, and the confidence levels of the information change ratios of different intersections may be different; the more obvious the real-time performance of the current intersection is, the more accurate the path direction judgment of each type of traffic information at the current intersection is.

[0090] So far, this embodiment has determined the path direction coefficient of each type of traffic information corresponding to the current intersection.

[0091] S4. Determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection according to the path direction coefficient of each type of traffic information corresponding to the current intersection.

[0092] During the generation process of the high-precision map, the display state of the road condition information is limited by the graphic area condition. The graphic area is a regional division block based on the map. For example, the building block area, lane area, sidewalk area, or vehicle area near the intersection, etc. The graphic area is used to display multi-type data information, and each type of traffic information has its corresponding graphic area, and the graphic area can be formed by the data collection area of each type of traffic information.

[0093] In this embodiment, the analysis of the display fineness of the road condition information on the graphic area is realized based on the graphic display condition performance of the current information. Therefore, based on the path direction coefficient of each type of traffic information corresponding to the current intersection, the display fineness of the road condition information on the graphic area can be quantitatively analyzed, which is realized through Figure 4 the steps S41 to S42 shown:

[0094] S41. Obtain the graphic area of each type of traffic information corresponding to the current intersection; for any type of traffic information, use the other types of traffic information that overlap with the graphic area of this type of traffic information as the relevant type of traffic information.

[0095] In this embodiment, the graphic area refers to the size of the area occupied by the ontology corresponding to each type of traffic information within the sensing range; for the j-th type of traffic information, other types of traffic information with overlapping areas with the graphic area of the j-th type of traffic information are regarded as the relevant type of traffic information corresponding to the j-th type of traffic information. Among them, the method for obtaining the graphic area is a prior art and not within the scope of protection of the present invention, so it will not be elaborated in detail here.

[0096] S42. Determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection according to the path direction coefficient of each relevant type of traffic information corresponding to each type of traffic information, and the overlapping area between the graphic area corresponding to each type of traffic information and the graphic areas of its respective relevant types of traffic information.

[0097] In this embodiment, the display fineness of the road condition information on the graphic area can also be the display detail. The higher the display detail, the more detailed road condition information should be displayed for the corresponding type of traffic information when generating the high-precision map; the larger the path direction coefficient, the higher the importance of the corresponding type of traffic information for path planning, and then the higher the display fineness of the road condition information on its corresponding graphic area; from the perspective of graphic display, the larger the overlapping area between the graphic area of the corresponding type of traffic information and the graphic areas of its relevant types of traffic information, the more predictable the movement of the graphic area, and there is no need to give too much attention, so the display fineness of the road condition information on the graphic area corresponding to the corresponding type of traffic information can be smaller.

[0098] The above step S42 can be implemented through steps S421 to S423 (not shown in the figure):

[0099] S421. For any relevant type of traffic information corresponding to any type of traffic information, determine the ratio of the path direction coefficient of this relevant type of traffic information of this type of traffic information to the normalized value of the overlapping area.

[0100] S422. For each relevant type of traffic information corresponding to this type of traffic information, calculate the average value of all the ratios corresponding to this type of traffic information to obtain the average ratio value.

[0101] S423. Perform normalization processing on the average ratio value to obtain the second normalized value, and use the second normalized value as the display fineness of the road condition information on the graphic area corresponding to this type of traffic information at the current intersection.

[0102] As an example, the calculation formula for the display fineness of the road condition information on the graphic area corresponding to the j-th type of traffic information at the current intersection can be:

[0103] ; in the formula, Represents the display fineness of the road condition information on the graphic area corresponding to the j-th type of traffic information at the current intersection. Represents the maximum-minimum normalization, that is, the maximum-minimum normalization within the range of all graphic areas at the same level. V represents the number of related types of traffic information corresponding to the j-th type of traffic information, and v represents the v-th related type of traffic information. Represents the path direction coefficient of the v-th related type of traffic information corresponding to the j-th type of traffic information at the current intersection. Represents the overlapping area between the graphic area corresponding to the j-th type of traffic information at the current intersection and the graphic area of its corresponding v-th related type of traffic information. The overlapping area refers to the number of pixel points where the two graphic areas intersect. Norm represents the normalization function.

[0104] In the calculation formula of the display fineness, Represents the ratio of the path direction coefficient and the overlapping area of the related type of traffic information. Generally, the overlapping area cannot be zero. However, to unify the dimensions of the numerator and denominator, the denominator overlapping area is normalized to obtain the normalized value of the overlapping area. ; Overlapping area The smaller it is, the less overlapping the graphic area corresponding to the j-th type of traffic information is with the graphic area of its v-th related type of traffic information. That is, the smaller the overlapping area, the more unpredictable the movement of the graphic area corresponding to the j-th type of traffic information, and more attention should be paid to its certain behavior. The display fineness of the road condition information on the graphic area corresponding to the j-th type of traffic information is greater. Represents the path direction coefficient of other types of traffic information that overlap with the graphic area corresponding to the j-th type of traffic information. In fact, the traffic information can play a navigation role depending on the display effect of the graphic information. Therefore, it is necessary to consider the display influence problem of multiple types of traffic information from the perspective of other types of traffic information that overlap with the graphic area corresponding to the j-th type of traffic information, that is, to quantitatively analyze the influence degree of the path direction of other types of traffic information that overlap with the graphic area corresponding to the j-th type of traffic information to determine the display fineness of the road condition information on the graphic area corresponding to the j-th type of traffic information. Represents the averaging process of the ratios of the path direction coefficients and the overlapping areas of all related types of traffic information of the j-th type of traffic information. The greater the average value of all ratios, the greater the display fineness of the road condition information on the graphic area corresponding to the j-th type of traffic information at the current intersection. Used to limit the value range of the display fineness to be within 0 to 1.

[0105] According to the above calculation method of the display fineness of the road condition information in the graphic area corresponding to the j-th type of traffic information at the current intersection, the display fineness of the road condition information in the graphic area corresponding to each type of traffic information at the current intersection can be obtained, and no repeated description will be given here.

[0106] So far, this embodiment has obtained the display fineness of the road condition information in the graphic area corresponding to each type of traffic information at the current intersection, and uses the display fineness as the information display fineness for the dynamic display of the graphic area. When dynamically updating the road condition information, it is displayed according to the information display fineness. Taking the information update method as an example, the information display fineness can be incrementally displayed at multiple levels of precision such as the approximate position of the lane lines, the position of the lane line markings, and the position of the multi-lane line markings in the lane area, so as to achieve the best display of the best road condition information on the graphic area, help users extract the required road condition information more quickly and accurately, and realize driving risk control.

[0107] Another embodiment of the present invention provides a high-precision map generation system based on a driving path. The system can implement a high-precision map generation method based on a driving path, as Figure 5 shown. The system includes:

[0108] A traffic information collection module 501, configured to obtain several types of traffic information corresponding to the current intersection in the vehicle driving path, where the traffic information includes static traffic information and dynamic traffic information;

[0109] An information coefficient determination module 502, configured to analyze the real-time influence of the traffic information at the current intersection on path planning according to each type of traffic information corresponding to the current intersection, and determine the real-time information influence coefficient of the current intersection;

[0110] A direction coefficient determination module 503, configured to analyze the influence degree of each type of traffic information on the path direction according to the real-time information influence coefficient of the current intersection, and determine the path direction coefficient of each type of traffic information corresponding to the current intersection;

[0111] A fineness determination module 504, configured to determine the display fineness of the road condition information in the graphic area corresponding to each type of traffic information at the current intersection according to the path direction coefficient of each type of traffic information corresponding to the current intersection.

[0112] Another embodiment of the present invention provides a high-precision map generation device based on a driving path, including a processor and a memory. The processor is used to process the instructions stored in the memory to implement a high-precision map generation method based on a driving path.

[0113] The present invention provides a high-precision map generation method, system and device based on a driving path. By analyzing the dynamic-static relationship in path requirements, the uncertain impact of its own dynamic traffic information is reduced, and the display fineness of road condition information on the graphic area corresponding to each type of traffic information is determined, so as to realize the display adjustment at the level of high-precision map information generation.

[0114] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A high-precision map generation method based on a driving route, characterized in that Including the following steps: Obtain several types of traffic information corresponding to the current intersection in the vehicle driving path, where the traffic information includes static traffic information and dynamic traffic information; Analyze the real-time impact of the traffic information of the current intersection on path planning according to each type of traffic information corresponding to the current intersection, and determine the real-time information impact coefficient of the current intersection, including: for the current intersection, determine the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information at the current moment compared with the previous moment; determine the current intersection as a node and the path as a connection between nodes to obtain the number of connections of the node corresponding to the current intersection; according to the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information, combined with the number of connections of the node corresponding to the current intersection, determine the real-time information impact coefficient of the current intersection; Analyze the impact degree of each type of traffic information on the path direction according to the real-time information impact coefficient of the current intersection, and determine the path direction coefficient of each type of traffic information corresponding to the current intersection, including: obtain the data update frequency of each type of traffic information at the current intersection in each connection direction, and then calculate the average value of the data update frequencies of each type of traffic information at the current intersection in all connection directions; according to the data update frequency of each type of traffic information at the current intersection in each connection direction and the average value of the data update frequencies, determine the information change proportion of each type of traffic information in multiple connection directions at the current intersection; according to the information change proportion and the real-time information impact coefficient of the current intersection, determine the path direction coefficient of each type of traffic information corresponding to the current intersection; Determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection according to the path direction coefficient of each type of traffic information corresponding to the current intersection, including: obtain the graphic area corresponding to each type of traffic information at the current intersection; for any type of traffic information, regard the other types of traffic information overlapping with the graphic area of this type of traffic information as related type traffic information; according to the path direction coefficients of each related type traffic information corresponding to each type of traffic information, and the overlapping area between the graphic area corresponding to each type of traffic information and the graphic areas of its respective related type traffic information, determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection.

2. The high-precision map generation method based on a driving route according to claim 1, wherein The determination method of the number of type changes includes: Obtain each type of undetermined traffic information at the current moment and each type of undetermined traffic information at the previous moment of the current moment, and count the number of new types and the number of disappeared types compared with each type of undetermined traffic information at the previous moment of the current moment; Take the value obtained by adding the number of new types and the number of disappeared types as the number of type changes corresponding to the undetermined traffic information, and the undetermined traffic information is static traffic information or dynamic traffic information.

3. The method for generating a high-precision map based on a driving route according to claim 2, wherein According to the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information, combined with the number of connections of the node corresponding to the current intersection, determine the real-time information impact coefficient of the current intersection, including: Analyze the relative relationship between static and dynamic changes based on the number of type changes corresponding to static and dynamic traffic information, and obtain the initial information coefficient of the current intersection. Use the number of connections of the nodes corresponding to the current intersection as the weight of the initial information coefficient, and perform weighted processing on the initial information coefficient to obtain the real-time information influence coefficient of the current intersection.

4. The method for generating a high-precision map based on a driving route according to claim 1, wherein Determine the path direction coefficient of each type of traffic information corresponding to the current intersection, including: For any type of traffic information, obtain the fusion value of the information change ratio and the real-time information influence coefficient of the current intersection. Perform normalization processing on the fusion value to obtain the first normalized value, and use the first normalized value as the path direction coefficient of the corresponding type of traffic information.

5. The method for generating a high-precision map based on a driving route according to claim 1, wherein Determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information of the current intersection, including: For any related type of traffic information corresponding to any type of traffic information, determine the ratio of the path direction coefficient of this related type of traffic information of this type of traffic information to the normalized value of the overlapping area. For each related type of traffic information corresponding to this type of traffic information, calculate the mean value of all the ratios corresponding to this type of traffic information to obtain the ratio mean value. Perform normalization processing on the ratio mean value to obtain the second normalized value, and use the second normalized value as the display fineness of the road condition information on the graphic area corresponding to this type of traffic information of the current intersection.

6. A high-precision map generation system based on a driving path, characterized in that, The system includes: A traffic information collection module, which is used to obtain several types of traffic information corresponding to the current intersection in the vehicle driving path, and the traffic information includes static traffic information and dynamic traffic information. An information coefficient determination module, which is used to analyze the real-time influence status of the traffic information of the current intersection on the path planning according to each type of traffic information corresponding to the current intersection, and determine the real-time information influence coefficient of the current intersection, including: for the current intersection, determine the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information at the current moment compared with the previous moment; determine the current intersection as a node and the path as the connection between nodes to obtain the number of connections of the nodes corresponding to the current intersection; according to the number of type changes corresponding to the static traffic information and the number of type changes corresponding to the dynamic traffic information, combined with the number of connections of the nodes corresponding to the current intersection, determine the real-time information influence coefficient of the current intersection. A direction coefficient determination module, which is used to analyze the influence degree of each type of traffic information on the path direction according to the real-time information influence coefficient of the current intersection, and determine the path direction coefficient of each type of traffic information corresponding to the current intersection, including: obtain the data update frequency of each type of traffic information of the current intersection in each connection direction, and then calculate the mean value of the data update frequencies of each type of traffic information of the current intersection in all connection directions; according to the data update frequency of each type of traffic information of the current intersection in each connection direction and the mean value of the data update frequencies, determine the information change ratio of each type of traffic information in multiple connection directions of the current intersection; according to the information change ratio and the real-time information influence coefficient of the current intersection, determine the path direction coefficient of each type of traffic information corresponding to the current intersection. The fineness determination module is used to determine the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection according to the path direction coefficient of each type of traffic information corresponding to the current intersection, including: obtaining the graphic area of each type of traffic information corresponding to the current intersection; for any type of traffic information, taking the other types of traffic information that overlap with the graphic area of this type of traffic information as the relevant type of traffic information; determining the display fineness of the road condition information on the graphic area corresponding to each type of traffic information at the current intersection according to the path direction coefficients of the respective relevant types of traffic information corresponding to each type of traffic information, and the overlapping area between the graphic area corresponding to each type of traffic information and the graphic areas of its respective relevant types of traffic information.

7. A high-precision map generation device based on a driving route, characterized in that, It includes a processor and a memory. The processor is used to process the instructions stored in the memory to implement a high-precision map generation method based on a driving path as described in any one of claims 1-5.

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