A method, apparatus, device and storage medium for generating a highway route

Abstract

This invention discloses a method, apparatus, device, and storage medium for generating highway routes. The method includes: acquiring an original raster topographic map containing elevation information and acquiring preset highway route design requirements; simplifying the original raster topographic map to obtain a low-precision raster topographic map; processing the low-precision raster topographic map using a preset path search algorithm to obtain a polyline route that meets the highway route design requirements; generating a Gaussian mixture model based on the polyline route and generating a probabilistic topographic map using the Gaussian mixture model; and processing the probabilistic topographic map using the path search algorithm to obtain a final highway route that meets the highway route design requirements. This invention can efficiently generate high-quality highway routes.

Description

Technical Field

[0001] This invention relates to the field of computer-aided design technology, and in particular to a method, apparatus, device, and storage medium for generating highway routes. Background Technology

[0002] In highway construction, route selection is the most critical and systematic step. The rationality of the route design directly impacts construction quality, project cost, and subsequent operational safety and driving comfort. However, in traditional highway route design, designers rely solely on topographic maps and geological survey reports, comprehensively considering factors such as the geographical location, natural conditions, engineering difficulty, social circumstances, and the nature, grade, and investment of the route. This reliance on experience alone results in lengthy design cycles and difficulty in ensuring quality.

[0003] Therefore, how to improve the efficiency of highway route generation while ensuring the quality of the generated routes has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, device, and storage medium for generating highway routes, which can improve the efficiency of highway route generation while ensuring the quality of the generated routes.

[0005] According to one aspect of the present invention, a method for generating highway routes is provided, the method comprising: Obtain the original raster topographic map containing elevation information, and obtain the preset highway route design requirements; The original raster topographic map is simplified to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map. The low-precision raster topographic map is processed using a preset path search algorithm to obtain a polyline route that meets the requirements of the highway route design. A Gaussian mixture model is generated based on the polygonal route, and a probabilistic terrain map is generated using the Gaussian mixture model. The probabilistic topographic map is processed using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

[0006] Optionally, the simplification process of the original raster topographic map to obtain a low-precision raster topographic map includes: The adjacent raster cells in the original raster topographic map are merged into a set number of raster cells, and the elevation information in the raster cells before merging is merged into the elevation information of the merged raster cell according to a preset fusion algorithm.

[0007] Optionally, the path search algorithm includes: The starting and ending points, route grade, and design speed of the highway are extracted from the aforementioned highway route design requirements; Based on the preset highway route design specifications, determine the alignment limit constraints and estimate the cost corresponding to the route grade and design speed; The linear limit value constraint and the estimated cost are used as the search cost of the path search algorithm, and the target route with the minimum search cost is determined between the starting and ending points of the highway in the target topographic map. Wherein, when the target topographic map is the low-precision raster topographic map, the target route is the polyline route; or, when the target topographic map is the probabilistic topographic map, the target route is the final highway route.

[0008] Optionally, generating a Gaussian mixture distribution model based on the polygonal line route includes: According to the preset sampling principle, N sampling points are determined from the folded line; Centered on each sampling point, N Gaussian distribution models are constructed based on preset variance and weight parameters; The N Gaussian distribution models are combined to form the mixed Gaussian distribution model.

[0009] Optionally, generating the probabilistic terrain map using the Gaussian mixture model includes: Using the Gaussian mixture distribution model, the probability density value of each grid cell is calculated on the original grid topographic map or the low-precision grid topographic map; wherein, the probability density value is used to characterize the likelihood of the grid cell being a road route; The probabilistic terrain map is drawn based on the calculated probability density value of each grid cell.

[0010] To achieve the above objectives, the present invention also provides an apparatus for generating highway routes, the apparatus comprising: The acquisition module is used to acquire the original raster topographic map containing elevation information and to acquire the preset highway route design requirements; A simplification module is used to simplify the original raster topographic map to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map. The first search module is used to process the low-precision raster topographic map using a preset path search algorithm to obtain a polyline route that meets the design requirements of the highway route. The generation module is used to generate a Gaussian mixture distribution model based on the polyline route, and to generate a probabilistic terrain map using the Gaussian mixture distribution model. The second search module is used to process the probabilistic topographic map using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

[0011] Optionally, the simplification module is further configured to: The adjacent raster cells in the original raster topographic map are merged into a set number of raster cells, and the elevation information in the raster cells before merging is merged into the elevation information of the merged raster cell according to a preset fusion algorithm.

[0012] Optionally, the generation module is further configured to: According to the preset sampling principle, N sampling points are determined from the broken line; with each sampling point as the center, N Gaussian distribution models are constructed according to the preset variance and weight parameters; the N Gaussian distribution models are combined to form the mixture Gaussian distribution model.

[0013] To achieve the above objectives, the present invention also provides a computer device, which specifically includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for generating highway routes described above.

[0014] To achieve the above objectives, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for generating highway routes described above.

[0015] This invention provides a method, apparatus, device, and storage medium for generating highway routes, proposing a two-stage highway route generation method. Its core idea is to use a two-stage algorithm to first rapidly generate route trends on a low-precision topographic map, and then perform fine-tuning on a high-precision topographic map. The first stage is a trend selection stage, which quickly obtains the route trend; the second stage is a precision selection stage, which searches for routes with higher accuracy and better quality based on the first stage. In this invention, the route is rapidly generated on a low-precision raster topographic map in the first stage, and then fine-tuned on a probabilistic topographic map in the second stage, thereby efficiently generating high-quality highway routes. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is an optional flowchart illustrating the method for generating highway routes provided in Embodiment 1. Figure 2 This is a schematic diagram of another optional process for the method of generating highway routes provided in Embodiment 1; Figure 3 This is a schematic diagram of an optional component structure of the device for generating highway routes provided in Embodiment 3; Figure 4 This is a schematic diagram of an optional hardware structure for the computer device provided in Embodiment 4. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0018] Example 1 This invention provides a method for generating highway routes, such as... Figure 1 As shown, the method specifically includes the following steps: Step S101: Obtain the original raster topographic map containing elevation information and obtain the preset highway route design requirements.

[0019] The original raster topographic map is a topographic representation map that stores elevation values ​​in matrix form. A raster topographic map is a digital map that stores and represents surface elevation information in the form of rasters (pixels or grids). Each raster cell (pixel) contains one or more data representing surface features (such as elevation) within that cell. However, in addition to elevation information, a raster topographic map may also contain other geographic coordinate information, such as latitude and longitude.

[0020] In addition, the highway route design requirements shall include at least one of the following: highway start and end points, route grade, design speed, and obstacle information.

[0021] Step S102: Simplify the original raster topographic map to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map.

[0022] In this embodiment, the accuracy of the original raster topographic map is reduced to decrease the amount of data contained in the raster cells of the raster topographic map.

[0023] Specifically, step S102 includes: The adjacent raster cells in the original raster topographic map are merged into a set number of raster cells, and the elevation information in the raster cells before merging is merged into the elevation information of the merged raster cell according to a preset fusion algorithm.

[0024] In this embodiment, when reducing the accuracy of a raster topographic map, adjacent raster cells in the original raster topographic map are merged. This results in a lower-accuracy topographic map with fewer raster cells while maintaining the same area in the original raster topographic map. For example, if the original raster topographic map contains 24 raster cells, four adjacent raster cells are merged to obtain a lower-accuracy raster topographic map containing six raster cells, while keeping the map's extent unchanged. Alternatively, when reducing the accuracy of a raster topographic map, the scale of the original raster topographic map is reduced to create a lower-accuracy topographic map while keeping the number of raster cells constant. This results in a raster topographic map with the same number of raster cells but a larger map extent (i.e., the map extent represented by one raster cell is larger). For example, if the original raster topographic map contains 24 raster cells, the map scale is reduced to obtain a raster topographic map that still contains 24 raster cells, but the extent of this raster topographic map is larger than that of the original raster topographic map. When the precision of a raster topographic map decreases, the amount of data contained in each raster cell decreases accordingly, thereby reducing the amount of data required for the search and improving search efficiency. Searching for the optimal route on a high-precision topographic map requires a significant amount of time and computational resources. However, if a rough route plan can be quickly found on a low-precision topographic map that retains its overall shape and trend, the search scope and time on the subsequent high-precision topographic map can be greatly reduced, thus achieving the goal of generating road routes quickly and relatively accurately.

[0025] Step S103: Process the low-precision raster topographic map using a preset path search algorithm to obtain a polyline route that meets the design requirements of the highway route.

[0026] In this embodiment, when designing a route, the designer calculates a polyline route from a low-precision raster topographic map using a preset path search algorithm, provided that the requirements for highway route design are met.

[0027] Specifically, step S103 includes: Step A1: Extract the highway start and end points, route grade, and design speed from the highway route design requirements; Step A2: Determine the alignment limit constraints and estimate the cost corresponding to the route grade and design speed according to the preset highway route design specifications; Step A3: Use the linear limit constraints and estimated cost as the search cost of the path search algorithm, and determine the polyline route with the minimum search cost between the start and end points of the highway in the low-precision raster topographic map.

[0028] In this embodiment, alignment limit constraints refer to the maximum and minimum values ​​specified for route alignment (such as curve radius, straight length, and gradient) in highway design. These constraints need to be quantified as part of the search cost to ensure that the generated route meets design specifications or user requirements. Cost estimation involves estimating the cost of different routes during the route search process, so that it can be quantified as another part of the search cost. Additionally, sampling points are key points on the route; these sampling points typically contain geographic coordinate information (such as latitude and longitude) and elevation information so that the algorithm can accurately assess the route cost.

[0029] Furthermore, considering both alignment limit constraints and cost estimation, these constraints and costs are quantified as the route search cost, and the route with the minimum cost between the start and end points is searched. Preferably, this process can be implemented using the Lazy Theta* algorithm to find a route scheme that satisfies both design specifications and is economically reasonable. Additionally, in the Lazy Theta* algorithm, sampling points are typically potential route points evaluated during the search process, constituting the search space explored by the algorithm. It should also be noted that in practical applications, other existing algorithms can also be used, such as the A* algorithm and its variants, and the D*Lite algorithm; no specific limitation is made here.

[0030] Furthermore, the step of using the linear limit constraints and estimated cost as the search cost of the path search algorithm, and determining the target route with the minimum search cost between the starting and ending points of the highway on the target topographic map, specifically includes: During the longitudinal profile search, a pre-defined greedy strategy is adopted. Given the local plane alignment, the elevation information of each sampling point is determined by minimizing the cost and constraints.

[0031] In this embodiment, the search space for three-dimensional terrain is reduced to two-dimensional space by the above-described method, thereby improving search efficiency. Furthermore, through longitudinal profile search, the algorithm can determine the longitudinal profile shape of the route, optimize the longitudinal profile design, and assist in route selection, ensuring that the final determined route meets the requirements of terrain conditions, driving safety, and economy, while also having low cost and high feasibility.

[0032] Step S104: Generate a Gaussian mixture distribution model based on the polygonal route, and use the Gaussian mixture distribution model to generate a probabilistic terrain map.

[0033] In a probabilistic topographic map, each point has a probability value determined by a Gaussian mixture distribution, reflecting the likelihood that the point will become part of the route. By adjusting the parameters of the Gaussian mixture distribution, the shape and extent of the probabilistic topographic map can be controlled, thereby influencing the fine-tuning of the route.

[0034] Specifically, step S104 includes: Step B1: Determine N sampling points from the folded line according to the preset sampling principle; Step B2: Using each sampling point as the center, construct N Gaussian distribution models according to the preset variance and weight parameters; Step B3: Combine the N Gaussian distribution models to form the mixed Gaussian distribution model.

[0035] Furthermore, step S104 also includes: Step B4: Using the Gaussian mixture distribution model, calculate the probability density value of each grid cell on the original grid topographic map or the low-precision grid topographic map; wherein the probability density value is used to characterize the likelihood of the grid cell being a road route; Step B5: Draw the probabilistic terrain map based on the calculated probability density value of each grid cell.

[0036] In this embodiment, after determining the polyline route with the minimum search cost from the low-precision topographic map, N sampling points are determined from the polyline route according to a preset sampling principle. N Gaussian distribution models are generated with each sampling point as the center: the mean of each Gaussian distribution is set as the coordinate of the sampling point, and then adjusted according to the terrain complexity. For example, a larger covariance matrix can be set for areas with significant terrain variations to increase the search range. Typically, it is initialized as a uniform distribution, meaning each Gaussian distribution has the same weight. Finally, parameter estimation is performed, updating the mixture weights, mean, and covariance matrix to maximize the likelihood of the sampling points, resulting in a Gaussian mixture model. The probabilistic topographic map generated by the Gaussian mixture model defines high-probability areas for path optimization, reducing unnecessary computation and improving search efficiency.

[0037] Furthermore, step B4 specifically includes: B41: Calculate the distance from the grid cell to the center of each Gaussian distribution; B42: Based on the formula of the Gaussian distribution function, substitute the distance from the grid cell to the center of the Gaussian distribution into the formula to calculate the initial probability density value of the grid cell under each Gaussian distribution model; B43: Combine all the obtained initial probability density values ​​according to their corresponding weight values ​​to obtain the final probability density value of the grid cell.

[0038] Step S105: Process the probabilistic topographic map using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

[0039] Specifically, step S105 includes: Step C1: Extract the highway start and end points, route grade, and design speed from the highway route design requirements; Step C2: Determine the alignment limit constraints and estimate the cost corresponding to the route grade and design speed according to the preset highway route design specifications; Step C3: Use the linear limit constraints and estimated cost as the search cost of the path search algorithm, and determine the final road route with the minimum search cost between the road start and end points on the probabilistic topographic map.

[0040] In this embodiment, the Lazy Theta* algorithm is again used on the probabilistic topographic map, combined with estimated construction costs and alignment limit constraints, to determine the road route with the minimum search cost. This road route is then recorded as the final road route. This process is performed on a high-precision topographic map, thus generating higher-quality route plans.

[0041] In this embodiment, as Figure 2 As shown, the process involves acquiring the original raster topographic map, along with topographic raster data, route level, design speed, highway start and end points, and obstacle information. In the first stage, the accuracy of the raster topographic map is reduced to minimize information labeling. Then, with the goal of minimizing cost, the cost is estimated based on the linear limit values ​​corresponding to the route level and design speed. A suitable polyline route is then quickly calculated using the Lazy Theta* algorithm. In the second stage, multiple Gaussian distribution models are constructed centered on the sampling points within the polyline route. These Gaussian models are then combined to obtain a Gaussian mixture model. A probabilistic topographic map is generated based on the Gaussian mixture model and the polyline route. Finally, combining the probabilistic topographic map with the high-precision raster topographic map, and again with the goal of minimizing cost, the Lazy Theta* algorithm is used to calculate the final highway route with the lowest search cost.

[0042] In this embodiment, to improve the efficiency of the route generation algorithm while ensuring the quality of the generated route, a two-stage highway route generation method is proposed. The core idea is to use a two-stage algorithm: the first stage is the rapid route selection stage, which quickly searches and generates polyline routes on a low-precision topographic map; the second stage is the precise route selection stage, which generates a probability map through the polyline route and makes fine adjustments on a high-precision topographic map in combination with the probability map, thereby obtaining a highway route with higher accuracy and better quality.

[0043] Example 2 This invention provides a method for generating highway routes, which specifically includes the following steps: Step 1: Obtain the original raster topographic map (i.e., elevation matrix), route start and end points, route classification, design speed, and other conditions; Step 2: The first search, on a low-resolution topographic map, considers a few necessary constraints to quickly generate route options, specifically including: a) First, reduce the original raster topographic map to lower its accuracy, thereby reducing the search space and improving search efficiency; b) Then, on the low-precision raster topographic map, using the Lazy Theta* algorithm, considering the linear limit constraints (following the specifications or specified by the user), and combined with the estimated cost, the constraints and cost are quantified as the route search cost, and the polyline path with the minimum cost between the start and end points is searched. c) When searching the longitudinal profile, a greedy strategy is adopted. Given the local horizontal alignment, the elevation of the point is determined by minimizing the cost and the cost after quantification of the constraints, thereby reducing the search space of the three-dimensional terrain to two-dimensional space and improving the search efficiency. d) The heuristic function is designed to estimate the cost of the minimum cost path from the endpoint to the current point using conventional road segments; e) Output the polyline path of stage one, that is, the sampling point sequence of the route generated in stage one.

[0044] Step 3: The second search involves generating a probabilistic topographic map around the polyline route output from Stage 1 on a high-precision raster topographic map. Fine-tuning of the polyline route is then performed on this probabilistic topographic map, specifically including: a) Construct probabilistic terrain maps based on the Gaussian mixture distribution model, and generate probabilistic terrain maps of controllable scale by using the sampling points in the broken line route as the centers of the Gaussian mixture distribution; b) Follow the specifications for reasonable limits of the alignment (or as specified by the user), while taking into account the estimated cost, and use the Lazy Theta* algorithm to search for the highway route with the lowest cost between the start and end points; c) Longitudinal profile search and heuristic function design, using the same method as the first search; d) Output the final highway path.

[0045] Using the method of this embodiment to design highway routes of different grades and different design speeds in different terrains has the following advantages: (1) Under the premise of meeting the basic requirements of the "Highway Route Design Specification", more reasonable and lower cost route schemes can be generated; (2) Highway route schemes can be generated in minutes, which greatly improves design efficiency compared with the original scheme design cycle of several days or even several months for designers.

[0046] Example 3 This invention provides an apparatus for generating highway routes, such as... Figure 3 As shown, the device specifically includes the following components: The acquisition module 301 is used to acquire the original raster topographic map containing elevation information and to acquire the preset highway route design requirements; The simplification module 302 is used to simplify the original raster topographic map to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map. The first search module 303 is used to process the low-precision raster topographic map using a preset path search algorithm to obtain a polyline route that meets the design requirements of the highway route. The generation module 304 is used to generate a Gaussian mixture distribution model based on the polyline route, and to generate a probabilistic terrain map using the Gaussian mixture distribution model. The second search module 305 is used to process the probabilistic topographic map using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

[0047] Optionally, the simplification module 302 is specifically used for: The adjacent raster cells in the original raster topographic map are merged into a set number of raster cells, and the elevation information in the raster cells before merging is merged into the elevation information of the merged raster cell according to a preset fusion algorithm.

[0048] Optionally, the path search algorithms in the first search module 303 and the second search module 305 specifically include: The starting and ending points, route grade, and design speed of the highway are extracted from the highway route design requirements; the alignment limit constraints and estimated cost corresponding to the route grade and design speed are determined according to the preset highway route design specifications; the alignment limit constraints and estimated cost are used as the search cost of the path search algorithm, and the target route with the minimum search cost is determined between the starting and ending points of the highway in the target topographic map. Wherein, when the target topographic map is the low-precision raster topographic map, the target route is the polyline route; or, when the target topographic map is the probabilistic topographic map, the target route is the final highway route.

[0049] Optionally, the generation module 304 is specifically used for: According to the preset sampling principle, N sampling points are determined from the broken line; with each sampling point as the center, N Gaussian distribution models are constructed according to the preset variance and weight parameters; the N Gaussian distribution models are combined to form the mixture Gaussian distribution model.

[0050] Furthermore, the second search module 305 is also used for: Using the Gaussian mixture distribution model, the probability density value of each grid cell is calculated on the original grid topographic map or the low-precision grid topographic map; wherein, the probability density value is used to characterize the likelihood of the grid cell being a road route; and the probability topographic map is drawn based on the calculated probability density value of each grid cell.

[0051] In this embodiment, to improve the efficiency of the route generation algorithm while ensuring the quality of the generated route, a two-stage highway route generation method is proposed. The core idea is to use a two-stage algorithm: the first stage is the rapid route selection stage, which quickly searches and generates polyline routes on a low-precision topographic map; the second stage is the precise route selection stage, which generates a probability map through the polyline route and makes fine adjustments on a high-precision topographic map in combination with the probability map, thereby obtaining a highway route with higher accuracy and better quality.

[0052] Example 4 This embodiment also provides a computer device, such as a smartphone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server, or cabinet server (including a standalone server or a server cluster composed of multiple servers), etc., capable of executing programs. Figure 4 As shown, the computer device 40 in this embodiment includes, but is not limited to, a memory 401 and a processor 402 that are communicatively connected to each other via a system bus. It should be noted that... Figure 4 Only a computer device 40 with components 401-402 is shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0053] In this embodiment, the memory 401 (i.e., the readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 401 may be an internal storage unit of the computer device 40, such as the hard disk or memory of the computer device 40. In other embodiments, the memory 401 may also be an external storage device of the computer device 40, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 40. Of course, the memory 401 may include both the internal storage unit and its external storage device of the computer device 40. In this embodiment, the memory 401 is typically used to store the operating system and various application software installed on the computer device 40. In addition, the memory 401 may also be used to temporarily store various types of data that have been output or will be output.

[0054] In some embodiments, processor 402 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. This processor 402 is typically used to control the overall operation of computer device 40.

[0055] Specifically, in this embodiment, the processor 402 is used to execute a program for generating a highway route stored in the memory 401. When the program for generating a highway route is executed, it performs the following steps: Obtain the original raster topographic map containing elevation information, and obtain the preset highway route design requirements; The original raster topographic map is simplified to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map. The low-precision raster topographic map is processed using a preset path search algorithm to obtain a polyline route that meets the requirements of the highway route design. A Gaussian mixture model is generated based on the polygonal route, and a probabilistic terrain map is generated using the Gaussian mixture model. The probabilistic topographic map is processed using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

[0056] For a detailed description of the above method steps, please refer to Example 1. This example will not be repeated here.

[0057] Example 5 This embodiment also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, app store, etc., which stores a computer program. When the computer program is executed by a processor, it implements the following method steps: Obtain the original raster topographic map containing elevation information, and obtain the preset highway route design requirements; The original raster topographic map is simplified to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map. The low-precision raster topographic map is processed using a preset path search algorithm to obtain a polyline route that meets the requirements of the highway route design. A Gaussian mixture model is generated based on the polygonal route, and a probabilistic terrain map is generated using the Gaussian mixture model. The probabilistic topographic map is processed using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

[0058] For a detailed description of the above method steps, please refer to the first embodiment. This embodiment will not repeat the details here.

[0059] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0060] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0061] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0062] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method of generating a highway route, characterized by, The method includes: Obtain the original raster topographic map containing elevation information, and obtain the preset highway route design requirements; The original raster topographic map is simplified to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map. The low-precision raster topographic map is processed using a preset path search algorithm to obtain a polyline route that meets the requirements of the highway route design. A Gaussian mixture model is generated based on the polygonal route, and a probabilistic terrain map is generated using the Gaussian mixture model. The probabilistic topographic map is processed using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

2. The method of generating a highway route according to claim 1, wherein, The simplification process of the original raster topographic map to obtain a low-precision raster topographic map includes: The adjacent raster cells in the original raster topographic map are merged into a set number of raster cells, and the elevation information in the raster cells before merging is merged into the elevation information of the merged raster cell according to a preset fusion algorithm.

3. The method of generating a highway route according to claim 1, wherein, The path search algorithm includes: The starting and ending points, route grade, and design speed of the highway are extracted from the aforementioned highway route design requirements; Based on the preset highway route design specifications, determine the alignment limit constraints and estimate the cost corresponding to the route grade and design speed; The linear limit value constraint and the estimated cost are used as the search cost of the path search algorithm, and the target route with the minimum search cost is determined between the starting and ending points of the highway in the target topographic map. Wherein, when the target topographic map is the low-precision raster topographic map, the target route is the polyline route; or, when the target topographic map is the probabilistic topographic map, the target route is the final highway route.

4. The method of generating a highway route according to claim 1, wherein, The generation of the Gaussian mixture distribution model based on the broken line route includes: According to the preset sampling principle, N sampling points are determined from the folded line; Centered on each sampling point, N Gaussian distribution models are constructed based on preset variance and weight parameters; The N Gaussian distribution models are combined to form the mixed Gaussian distribution model.

5. The method for generating highway routes according to claim 4, characterized in that, The generation of probabilistic terrain maps using the Gaussian mixture model includes: Using the Gaussian mixture distribution model, the probability density value of each grid cell is calculated on the original grid topographic map or the low-precision grid topographic map; wherein, the probability density value is used to characterize the likelihood of the grid cell being a road route; The probabilistic terrain map is drawn based on the calculated probability density value of each grid cell.

6. An apparatus for generating a highway route, characterized by The device includes: The acquisition module is used to acquire the original raster topographic map containing elevation information and to acquire the preset highway route design requirements; A simplification module is used to simplify the original raster topographic map to obtain a low-precision raster topographic map; wherein the low-precision raster topographic map contains less data than the original raster topographic map. The first search module is used to process the low-precision raster topographic map using a preset path search algorithm to obtain a polyline route that meets the design requirements of the highway route. The generation module is used to generate a Gaussian mixture distribution model based on the polyline route, and to generate a probabilistic terrain map using the Gaussian mixture distribution model. The second search module is used to process the probabilistic topographic map using the path search algorithm to obtain the final highway route that meets the highway route design requirements.

7. The apparatus for generating a highway route according to claim 6, wherein, The generation module is used for: According to the preset sampling principle, N sampling points are determined from the folded line; Centered on each sampling point, N Gaussian distribution models are constructed based on preset variance and weight parameters; The N Gaussian distribution models are combined to form the mixed Gaussian distribution model.

8. The apparatus for generating a highway route according to claim 7, wherein, The generation module is further configured to: Using the Gaussian mixture distribution model, the probability density value of each grid cell is calculated on the original grid topographic map or the low-precision grid topographic map; wherein, the probability density value is used to characterize the likelihood of the grid cell being a road route; The probabilistic terrain map is drawn based on the calculated probability density value of each grid cell.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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