A test route mapping system fusing multi-source environmental features

Abstract

The application provides a test route mapping system fusing multi-source environment characteristics, relates to the technical field of transportation, and comprises a basic data acquisition module configured to acquire target area data and original area data; a multi-dimensional area screening module configured to screen temperature, altitude and road network according to the target area data and the original area data to obtain an initial planning road network; a starting point screening module configured to screen a spatial grid in the initial planning road network to obtain a plurality of initial starting points; a test route planning module configured to generate candidate routes with all the initial starting points as base points to obtain an initial route library; and an optimal route screening module configured to calculate scores and screening according to altitude average deviation characteristics and speed deviation characteristics to obtain an optimal recurring target area test route. The above system solves the problems of vehicle problem troubleshooting, energy consumption development and emission verification for different areas in the prior art.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, and in particular to a test route mapping system that integrates multi-source environmental features. Background Technology

[0002] In the process of vehicle research and development and verification, conducting road tests in areas with special geographical and climatic environments is a crucial step in ensuring that the vehicle's adaptability, reliability, and overall performance meet the standards. Because the target areas involved in such tests often have significant unique characteristics in terms of natural environment and road conditions, and conventional test sites and resource layouts cannot directly cover them, traditional testing methods usually face problems such as lengthy implementation cycles, huge coordination costs, and low testing efficiency.

[0003] In practical development and problem diagnosis, related needs are becoming increasingly prominent: when a vehicle model experiences recurring performance anomalies or malfunctions in areas with specific road and environmental characteristics (such as continuous uphill climbing, extreme temperatures, or complex altitude changes), there is an urgent need to find a road route within the testing resources readily available to the development team that is highly similar to the target area in terms of environmental characteristics and driving conditions, in order to effectively reproduce, diagnose, and optimize the problem. Similarly, to complete the development and verification of specific performance characteristics such as energy consumption and emissions of vehicles under specific environments, it is also necessary to construct alternative routes that match the original test routes in terms of key environmental and driving characteristics under existing testing resources.

[0004] However, current widely used test route planning methods are primarily designed based on conventional road conditions and general driving behavior patterns, failing to establish systematic quantitative matching models for the multidimensional and complex characteristics of specific target routes. In particular, existing methods lack effective feature extraction and high-precision matching capabilities in key dimensions reflecting continuous altitude changes, temperature and humidity distribution patterns, typical speed ranges, and their dynamic processes. This results in significant deviations between the planned routes and the original target routes in terms of environmental stress and driving conditions, making it difficult to accurately reproduce the comprehensive technical challenges posed by specific areas. Consequently, these methods cannot effectively support the precise location and improvement of related vehicle problems and also restrict the reliable verification of performance metrics such as energy consumption and emissions under equivalent conditions. Summary of the Invention

[0005] This application provides a test route mapping system that integrates multi-source environmental characteristics to solve the problems of existing technologies that cannot perform vehicle problem investigation, energy consumption development, and emission verification in different regions.

[0006] The system includes: The basic data acquisition module is configured to acquire target area data of the route to be tested in the target area and original area data of the original area; the original area includes several sub-areas, and the original area data includes corresponding data for each sub-area; A multi-dimensional region filtering module is configured to sequentially filter all the sub-regions in the original region based on the target region data and the original region data, using temperature, altitude, and road network data, to obtain an initial planned road network. The starting point filtering module is configured to filter spatial grids in the initial planned road network based on the route to be tested, and obtain several initial starting points. The test route planning module is configured to generate candidate routes based on a path planning method that uses all the initial starting points as base points and parallel branch exploration and two-layer screening to obtain an initial route library; the initial route library includes several qualified candidate routes. The optimal route selection module is configured to calculate and select the optimal test route for reproducing the target area based on the average elevation deviation characteristics of all qualified candidate routes and the speed deviation characteristics between each qualified candidate route and the route to be tested.

[0007] In some implementations, the basic data acquisition module includes: A target area data acquisition unit is configured to acquire second-by-second actual driving data of the test route in the target area; the second-by-second actual driving data includes time, vehicle speed, temperature, and regional altitude; and to perform feature calculations based on the second-by-second actual driving data to obtain the target area data. The original region data acquisition unit is configured to acquire the elevation dataset and road network dataset of the original region; perform coordinate point matching and querying one by one based on the road network dataset and the elevation dataset, and integrate the temperature information of the original region to obtain the original region data.

[0008] In some implementations, the target area data includes mileage-altitude change data, mileage-speed data, total mileage of the test route, highest altitude point, lowest altitude point, highest temperature, and lowest temperature; The original area data includes road network elevation data and original area temperature data; the road network elevation data table is used to represent the elevation of each coordinate point in the road network.

[0009] In some implementations, the multi-dimensional region filtering module includes: A regional temperature filtering unit is configured to select several sub-regions that match the original region based on the target region data, and to filter all the sub-regions based on the highest and lowest temperatures in the target region data and the highest and lowest temperatures in the original region data to obtain several candidate test regions. A regional altitude filtering unit is configured to filter each road in all the candidate test areas based on the highest and lowest altitude points in the target area data, and the highest and lowest altitude points of each road in the candidate test areas, to obtain a number of candidate roads. A regional road network filtering unit is configured to extract all candidate roads to form a road network; divide the road network into multiple connected regions using a connected component algorithm; and remove the connected regions whose total road network length does not meet the road network length threshold to obtain the initial planned road network.

[0010] In some implementations, the starting point filtering module includes: Based on the route to be tested, road nodes whose altitude error with the target value is within the altitude error threshold are extracted from the initial planned road network as candidate starting points; According to the grid division method, the candidate starting points are divided into grid cells of the same size according to their spatial location. Non-highway road connection points are screened as valid candidate points, and a certain number of the valid candidate points in the grid are randomly selected as the initial starting point.

[0011] In some implementations, the test route planning module includes: An initial search unit deployment unit is configured to deploy a path search unit at each initial starting point and record the status parameters of the path search unit. A parallel exploration and branch expansion unit is configured to deploy a new path search unit on each branch corresponding to a road node when path exploration is performed from the initial starting point and a road node appears, and to use the newly deployed path search unit to inherit the state parameters of the path search unit on the original path. The filtering and determination unit is configured to sequentially filter the paths corresponding to all the path search units by average deviation threshold and mileage to obtain the initial route library.

[0012] In some implementations, the filtering and determination unit includes: A filtering component is configured to calculate the average cumulative altitude deviation of all the path search units and compare it with the average cumulative altitude deviation according to an average deviation threshold; to filter the path search units that pass the average deviation threshold by cumulative mileage, to include the path search units that have not exceeded the target mileage into the search unit library, and to include the path search units that have exceeded the target mileage into the initial route library. The search deduplication component is configured to filter the path search units in the search unit library according to the cumulative altitude deviation and use them as the path search units for the next round of path search. A qualified screening component is configured to stop path searching and output the initial route library when any one of the preset qualified screening requirements is met.

[0013] In some implementations, the preset qualification screening requirements include: The path search iteration count has reached the iteration count threshold; The number of qualified routes in the initial route database has reached the threshold for the number of qualified routes; The search unit library does not contain any path search units that meet the requirements of the first round of screening.

[0014] In some implementations, the optimal route selection module includes: A route extraction unit is configured to select several qualified routes with the smallest cumulative altitude deviation from the initial route database and obtain the average driving speed of each route. An altitude average deviation characteristic calculation unit is configured to calculate the altitude average deviation characteristics of all selected qualified routes. A speed deviation feature calculation unit is configured to calculate the speed deviation feature of each qualified route based on the average driving speed. The scoring calculation unit is configured to calculate a score based on the speed deviation characteristics and the average altitude deviation characteristics, and to select the route with the highest score to obtain the optimal test route for reproducing the target area.

[0015] In some implementations, the scoring calculation unit is further configured as follows: The speed deviation characteristics and the average altitude deviation characteristics are normalized and weighted scoring is performed to obtain the optimal test route for reproducing the target area.

[0016] As described above, this application provides a test route mapping system that integrates multi-source environmental features. The system includes a basic data acquisition module configured to acquire target area data of the route to be tested within a target area and original area data of the original area. The original area includes several sub-areas, and the original area data includes corresponding data for each sub-area. A multi-dimensional area filtering module is configured to sequentially filter all sub-areas in the original area based on temperature, altitude, and road network according to the target area data and the original area data to obtain an initial planned road network. A starting point filtering module is also included. To obtain several initial starting points by filtering spatial grids in the initial planned road network based on the route to be tested, a test route planning module is configured to generate candidate routes using a path planning method based on parallel branch exploration and two-layer filtering, using all the initial starting points as base points, to obtain an initial route library. The initial route library includes several qualified candidate routes. An optimal route selection module is configured to calculate and select the optimal test route for reproducing the target area based on the average elevation deviation characteristics of all qualified candidate routes and the speed deviation characteristics between each qualified candidate route and the route to be tested. This application solves the problems of existing technologies being unable to conduct vehicle problem investigation, energy consumption development, and emission verification in different regions through the above system. Attached Figure Description

[0017] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a test route mapping system that integrates multi-source environmental features according to this application; Figure 2 This is a schematic diagram of the basic data acquisition module in a test route mapping system that integrates multi-source environmental features according to this application; Figure 3 This is a schematic diagram of a multi-dimensional region filtering module in a test route mapping system that integrates multi-source environmental features according to this application; Figure 4 This is a schematic diagram of the test route planning module in a test route mapping system that integrates multi-source environmental features according to this application; Figure 5 This is a schematic diagram of the screening and judgment unit in a test route mapping system that integrates multi-source environmental features according to this application. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0021] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0022] See Figures 1 to 5 As can be seen, this embodiment provides a test route mapping system that integrates multi-source environmental features, the system comprising: The basic data acquisition module is configured to acquire target area data of the route to be tested in the target area and original area data of the original area; the original area includes several sub-areas, and the original area data includes corresponding data for each of the sub-areas.

[0023] Specifically, in this embodiment, the basic data acquisition module includes: A target area data acquisition unit is configured to acquire second-by-second actual driving data of the test route in the target area; the second-by-second actual driving data includes time, vehicle speed, temperature, and regional altitude; and to perform feature calculations based on the second-by-second actual driving data to obtain the target area data.

[0024] Specifically, the target area data acquisition unit acquires second-by-second actual driving data of the test route in the target area, with parameters including at least time, vehicle speed, altitude, and temperature. Based on the second-by-second actual driving data of the test route in the target area, mileage-altitude change data is calculated. Based on the speed and time curves in the vehicle driving data, mileage-speed data and the total mileage of the test route are calculated. The highest and lowest altitude points of the test route in the target area are extracted. At the same time, the highest and lowest temperatures of the areas traversed by the test route in the target area are extracted.

[0025] For example, the target area test route data obtained in this embodiment is sampled at a frequency of 1Hz, and is a test route located in a certain area.

[0026] For example, the target area test route mileage-altitude change selection ranges from 40 meters to 611 meters. Calculations show that the highest altitude point is 611 meters and the lowest altitude point is 40 meters (located at the starting point). The test was conducted in winter, with the highest temperature in the area being 7°C and the lowest being -5°C.

[0027] The basic data acquisition module also includes: The original region data acquisition unit is configured to acquire the elevation dataset and road network dataset of the original region; perform coordinate point matching and querying one by one based on the road network dataset and the elevation dataset, and integrate the temperature information of the original region to obtain the original region data.

[0028] Specifically, the original region elevation dataset and the original region road network dataset are obtained through the original region data acquisition unit.

[0029] The elevation dataset should include point-by-point elevation values ​​within the original region; the road network dataset should cover core attributes such as road orientation, road nodes, road segments, start and end nodes of road segments, and road length.

[0030] Based on the latitude and longitude coordinates in the road network dataset, a match query is performed one by one from the elevation dataset. Specifically, elevation sampling is performed along each road at a set distance to obtain elevation change data along the road; at the same time, the corresponding elevation values ​​of all road nodes are extracted.

[0031] For example, the original regional elevation dataset uses the GLO-30 dataset released in 2020; the original regional road network dataset uses OpenStreetMap open-source vector road network data.

[0032] The system also includes: A multi-dimensional region filtering module is configured to sequentially filter all sub-regions in the original region based on temperature, altitude, and road network according to the target region data and the original region data to obtain an initial planned road network.

[0033] Specifically, in this embodiment, the multi-dimensional region filtering module includes: A regional temperature filtering unit is configured to select several matching sub-regions in the original region based on the target region data, and to filter all the sub-regions based on the highest and lowest temperatures in the target region data and the highest and lowest temperatures in the original region data to obtain several candidate test regions.

[0034] Specifically, the regional temperature screening unit selects sub-regions with similar climates from the original region as candidate regions based on the environmental temperature characteristics of the test route in the target region.

[0035] The average daily temperature of each sub-region over the course of a year is calculated, and the 5th and 95th percentiles are used as the lowest and highest temperatures of that sub-region. Sub-regions that meet the following conditions are selected: the highest temperature of the sub-region is greater than the highest temperature of the target area test route, and the lowest temperature of the sub-region is less than the lowest temperature of the target area test route. Sub-regions that meet the conditions are selected as candidate test areas.

[0036] The multi-dimensional region filtering module also includes: A regional altitude filtering unit is configured to filter each road in all the candidate test areas based on the highest and lowest altitude points in the target area data, and the highest and lowest altitude points of each road in the candidate test areas, to obtain a number of candidate roads.

[0037] Specifically, the regional altitude screening unit calculates the highest and lowest altitudes of each road in the candidate test area based on the extracted highest and lowest altitudes of the target area test route, and screens roads that meet the following conditions: the lowest altitude of the road should be greater than or equal to the lowest point of the target area test route minus the altitude deviation threshold, and the highest altitude of the road should be less than or equal to the highest point of the target area test route plus the altitude deviation threshold; roads that do not meet the above conditions are eliminated.

[0038] In this embodiment, the altitude deviation threshold is set to 100 meters, and the roads with altitude changes ranging from -60 to 711 meters are selected to meet the altitude change characteristics of the test route in the target area.

[0039] The multi-dimensional region filtering module also includes: A regional road network filtering unit is configured to extract all candidate roads to form a road network; divide the road network into multiple connected regions using a connected component algorithm; and remove the connected regions whose total road network length does not meet the road network length threshold to obtain the initial planned road network.

[0040] Specifically, the regional road network filtering unit uses latitude and longitude coordinates from the road network data to perform a match query from the altitude dataset. The method involves sampling altitudes along each road at predetermined intervals to obtain elevation change data along the road; simultaneously, the corresponding altitude values ​​for all road nodes are extracted.

[0041] In this embodiment, the road network length threshold is set at 300 kilometers, and the longest connecting area is selected as the initial planned road network.

[0042] The system also includes: The starting point filtering module is configured to filter spatial grids in the initial planned road network based on the route to be tested, and obtain several initial starting points.

[0043] Specifically, in this embodiment, the starting elevation of the test route in the target area is used as a benchmark, and road nodes whose elevation error with the target value is within the elevation error threshold are extracted from the initial planned road network as candidate starting points.

[0044] The grid division method is adopted to divide the candidate starting points into grid cells of the same size according to their spatial location, and non-highway road connection points are selected as valid candidate points. A certain number of valid candidate points are randomly selected from the valid candidate points in each grid as the initial starting point for subsequent route exploration.

[0045] In this embodiment, the altitude error between the target value and the altitude error threshold is used as a reference. The initial planned road network contains 45,121 nodes and 52,312 roads. After screening, 35,264 candidate starting points are extracted.

[0046] In this embodiment, a 10km×10km grid unit is used, with 30 nodes randomly selected in each grid, resulting in a total of 24,300 initial starting points.

[0047] The system also includes: The test route planning module is configured to generate candidate routes based on a path planning method that uses all the initial starting points as base points and parallel branch exploration and two-layer screening to obtain an initial route library; the initial route library includes several qualified candidate routes.

[0048] Specifically, in this embodiment, the test route planning module includes: An initial search unit deployment unit is configured to deploy a path search unit at each initial starting point and record the status parameters of the path search unit.

[0049] The initial search unit deployment unit starts from a determined initial starting point, and one path search unit is deployed at each starting point. The status parameters of each unit are recorded, including the current node position, cumulative mileage, cumulative altitude deviation and path node sequence.

[0050] The test route planning module also includes: A parallel exploration and branch expansion unit is configured to deploy a new path search unit on each branch corresponding to a road node when path exploration is performed from the initial starting point and a road node appears, and to use the newly deployed path search unit to inherit the state parameters of the path search unit on the original path.

[0051] In this process, all path search units synchronously initiate path exploration through the parallel exploration and branch expansion unit. When a road node is reached, the vehicle branches along all reachable and non-repeated directions. Each branch generates a new path search unit, inheriting and updating the state parameters of the original unit: the current node position is updated to the new road node reached by the branch, the cumulative mileage is increased by the distance traveled by the branch, and the cumulative altitude deviation is increased by the absolute value of the altitude deviation of the branch segment. The absolute value of the altitude deviation is calculated by sampling an altitude point at set intervals along the branch segment, calculating the absolute value of the difference between the altitude of each sampling point and the altitude of the corresponding mileage point on the test route in the target area, and summing the absolute values ​​of the differences of all sampling points.

[0052] In this embodiment, the altitude sampling interval is set to 100 meters, that is, an altitude point is sampled every 100 meters along the branch road section, and the absolute value of the difference between the altitude of each sampling point and the altitude of the corresponding mileage point of the test route in the target area is calculated and summed.

[0053] The test route planning module also includes: The filtering and determination unit is configured to sequentially filter the paths corresponding to all the path search units by average deviation threshold and mileage to obtain the initial route library.

[0054] The screening and determination unit includes: A filtering component is configured to calculate the average cumulative altitude deviation of all the path search units and compare it with the average cumulative altitude deviation according to an average deviation threshold; to filter the path search units that pass the average deviation threshold by cumulative mileage, to include the path search units that have not exceeded the target mileage into the search unit library, and to include the path search units that have exceeded the target mileage into the initial route library.

[0055] The filtering is performed through the filtering component: Deviation threshold elimination: The new path search units generated after branch expansion are screened in the first round, and their average cumulative altitude deviation per unit mileage is calculated (average cumulative altitude deviation per unit mileage = cumulative altitude deviation / cumulative mileage); if the deviation exceeds the average elimination threshold, they are eliminated directly.

[0056] Mileage Screening: For path search units that pass the deviation threshold screening, if the cumulative mileage does not exceed the target mileage, they are included in the search unit library to continue participating in subsequent explorations. If the cumulative mileage reaches the target mileage, it is determined to be a qualified route and included in the initial route library, and the total number of qualified routes is counted. The target mileage is the total mileage of the test routes in the target area.

[0057] The filtering and determination unit further includes: The search deduplication component is configured to filter the path search units in the search unit library according to the cumulative altitude deviation and use them as the path search units for the next round of path search.

[0058] Specifically, the search deduplication component applies a same-location, same-mileage deduplication rule to all path search units in the search unit library. For multiple path search units that reach the same road node and have the same cumulative mileage range, only the path search unit with the smallest cumulative altitude deviation is retained as the optimal record for that node and mileage. Other path search units are removed from the search unit library and do not participate in subsequent explorations. If in subsequent rounds there are path search units that reach the same road node and have the same cumulative mileage range, if the cumulative altitude deviation of the path search unit is greater than or equal to the optimal record for that node and mileage, the path search unit is deleted; if the cumulative altitude deviation of the path search unit is less than the optimal record for that node and mileage, the path search unit is retained and the optimal record for that node and mileage is updated.

[0059] The remaining valid path search units in the search unit library are divided into multiple intervals based on the cumulative mileage. Within each mileage interval, a certain number of path search units with the smallest cumulative altitude deviation are selected as the next round of exploration units. At the same time, the selected path search units are removed from the search unit library to avoid repeated exploration of the same path.

[0060] The filtering and determination unit further includes: A qualified screening component is configured to stop path searching and output the initial route library when any one of the preset qualified screening requirements is met.

[0061] The above process is repeated until the qualified screening component determines that any of the following termination conditions are met: (1) If the number of iterations reaches the iteration threshold, the exploration shall be stopped regardless of whether a sufficient initial route has been obtained; (2) If the number of qualified routes in the initial route library reaches the threshold for the number of qualified routes, the exploration should be stopped immediately; (3) If there are no available path search units in the search unit library, path exploration cannot continue and the iteration will automatically terminate.

[0062] After the iteration ends, all qualified routes in the initial route library are organized to form a set of candidate routes that meet the preset requirements.

[0063] In this embodiment, the threshold for the number of iterations is set to 15,000 and the threshold for the number of qualified routes is 1,000. After 3,800 iterations, 1,002 qualified routes are found in the initial route library, which meets the termination condition. From the 1,002 qualified routes, the 30 routes with the smallest cumulative altitude deviation are selected as candidate routes.

[0064] The system also includes: The optimal route selection module is configured to calculate and select the optimal test route for reproducing the target area based on the average elevation deviation characteristics of all qualified candidate routes and the speed deviation characteristics between each qualified candidate route and the route to be tested.

[0065] Specifically, in this embodiment, the optimal route selection module includes: The route extraction unit is configured to select several qualified routes with the smallest cumulative altitude deviation from the initial route library and obtain the average driving speed of each route.

[0066] Specifically, the route extraction unit selects 30 candidate routes with the smallest cumulative altitude deviation from the initial route database, and obtains the average driving speed of each segment of each candidate route from the map service provider.

[0067] The optimal route selection module also includes: An altitude average deviation feature calculation unit is configured to calculate the altitude average deviation feature of all selected qualified routes.

[0068] The average altitude deviation of all candidate routes is calculated by the average altitude deviation feature calculation unit. The average altitude deviation of candidate routes = cumulative altitude deviation of candidate routes / total mileage of candidate routes.

[0069] The optimal route selection module also includes: A speed deviation feature calculation unit is configured to calculate the speed deviation feature of each qualified route based on the average driving speed.

[0070] Specifically, the speed deviation feature calculation unit calculates the mileage-speed data of the candidate routes based on the length of each road segment and the average driving speed obtained from the map service provider.

[0071] The speed deviation between the candidate route and the test route in the target area is calculated according to the following rules: (1) For each kilometer interval, if the average speed of the candidate route is greater than or equal to the average speed of the corresponding interval of the test route in the target area, the speed deviation of that interval is set to 0. (2) For each kilometer interval, if the average speed of the candidate route is lower than the average speed of the corresponding interval of the test route in the target area, then the speed deviation of the interval is the average speed of the corresponding interval of the test route in the target area minus the average speed of the candidate route. (3) Calculate the absolute value of the average speed deviation of all mileage intervals as the speed deviation value of the candidate route.

[0072] The optimal route selection module also includes: The scoring calculation unit is configured to calculate a score based on the speed deviation characteristics and the average altitude deviation characteristics, and to select the route with the highest score to obtain the optimal test route for reproducing the target area.

[0073] The scoring calculation unit normalizes the speed deviation and average altitude deviation characteristics of candidate routes, constructing a standardized scoring function S=100-α×H+β×V, where H is the normalized value of the average altitude deviation characteristic, V is the normalized value of the speed deviation value, α and β are weighting coefficients, both ranging from [0, 100], and α+β=1000, set by default according to test priority, and can be dynamically adjusted according to actual test needs. A comprehensive score is calculated for each candidate route, with the score range mapped to [0, 100] points; a higher score indicates stronger route adaptability. The route with the highest score is selected as the optimal original regionalized test route. In this embodiment, α=60, β=40, and the comprehensive score S=92.0 points, which is selected as the optimal original regionalized test route.

[0074] This embodiment has the following advantages: This embodiment provides a test route mapping system that integrates multi-source environmental features. Using a specific test route in a target area as a benchmark, it integrates altitude and road network data to establish a multi-dimensional regional screening system, optimizes route planning algorithms, and constructs a multi-dimensional route evaluation system that integrates altitude fit and speed matching. This method can efficiently generate original regionalized test routes that highly match the environmental and driving characteristics of the specific test route in the target area. It ensures that the original regionalized test routes are highly matched with the specific test routes in the target area in terms of altitude variation and driving speed characteristics, supporting vehicle problem investigation, energy consumption development, and emission verification, and improving the representativeness and transferability of test results.

[0075] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the discussion in some embodiments is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the contents of this disclosure, thereby enabling those skilled in the art to better utilize the embodiments.

Claims

1. A test route mapping system integrating multi-source environmental features, characterized in that, The system includes: The basic data acquisition module is configured to acquire target area data of the route to be tested in the target area and original area data of the original area; the original area includes several sub-areas, and the original area data includes corresponding data for each sub-area; A multi-dimensional region filtering module is configured to sequentially filter all the sub-regions in the original region based on the target region data and the original region data, using temperature, altitude, and road network data, to obtain an initial planned road network. The starting point filtering module is configured to filter spatial grids in the initial planned road network based on the route to be tested, and obtain several initial starting points. The test route planning module is configured to generate candidate routes based on a path planning method that uses all the initial starting points as base points and parallel branch exploration and two-layer screening to obtain an initial route library; the initial route library includes several qualified candidate routes. The optimal route selection module is configured to calculate and select the optimal test route for reproducing the target area based on the average elevation deviation characteristics of all qualified candidate routes and the speed deviation characteristics between each qualified candidate route and the route to be tested. The test route planning module includes: An initial search unit deployment unit is configured to deploy a path search unit at each initial starting point and record the status parameters of the path search unit. A parallel exploration and branch expansion unit is configured to deploy a new path search unit on each branch corresponding to a road node when path exploration is performed from the initial starting point and a road node appears, and to use the newly deployed path search unit to inherit the state parameters of the path search unit on the original path. The filtering and determination unit is configured to sequentially filter the paths corresponding to all the path search units by average deviation threshold and mileage to obtain the initial route library.

2. The test route mapping system integrating multi-source environmental features according to claim 1, characterized in that, The basic data acquisition module includes: A target area data acquisition unit is configured to acquire second-by-second actual driving data of the test route in the target area; the second-by-second actual driving data includes time, vehicle speed, temperature, and regional altitude; and to perform feature calculations based on the second-by-second actual driving data to obtain the target area data. The original region data acquisition unit is configured to acquire the elevation dataset and road network dataset of the original region; perform coordinate point matching and querying one by one based on the road network dataset and the elevation dataset, and integrate the temperature information of the original region to obtain the original region data.

3. The test route mapping system integrating multi-source environmental features according to claim 2, characterized in that, The target area data includes mileage-altitude change data, mileage-speed data, total mileage of the test route, highest altitude point, lowest altitude point, highest temperature, and lowest temperature. The original area data includes road network elevation data and original area temperature data; the road network elevation data table is used to characterize the elevation of each coordinate point in the road network.

4. The test route mapping system integrating multi-source environmental features according to claim 3, characterized in that, The multi-dimensional region filtering module includes: A regional temperature filtering unit is configured to select several sub-regions that match the original region based on the target region data, and to filter all the sub-regions based on the highest and lowest temperatures in the target region data and the highest and lowest temperatures in the original region data to obtain several candidate test regions. A regional altitude filtering unit is configured to filter each road in all the candidate test areas based on the highest and lowest altitude points in the target area data, and the highest and lowest altitude points of each road in the candidate test areas, to obtain a number of candidate roads. A regional road network filtering unit is configured to extract all candidate roads to form a road network; divide the road network into multiple connected regions using a connected component algorithm; and remove the connected regions whose total road network length does not meet the road network length threshold to obtain the initial planned road network.

5. A test route mapping system integrating multi-source environmental features according to claim 1, characterized in that, The starting point filtering module includes: Based on the route to be tested, road nodes whose altitude error with the target value is within the altitude error threshold are extracted from the initial planned road network as candidate starting points; According to the grid division method, the candidate starting points are divided into grid cells of the same size according to their spatial location. Non-highway road connection points are screened as valid candidate points, and a certain number of the valid candidate points in the grid are randomly selected as the initial starting point.

6. A test route mapping system integrating multi-source environmental features according to claim 1, characterized in that, The screening and determination unit includes: A filtering component is configured to calculate the average cumulative altitude deviation of all the path search units and compare it with the average cumulative altitude deviation according to an average deviation threshold; to filter the path search units that pass the average deviation threshold by cumulative mileage, to include the path search units that have not exceeded the target mileage into the search unit library, and to include the path search units that have exceeded the target mileage into the initial route library. The search deduplication component is configured to filter the path search units in the search unit library according to the cumulative altitude deviation and use them as the path search units for the next round of path search. A qualified screening component is configured to stop path searching and output the initial route library when any one of the preset qualified screening requirements is met.

7. A test route mapping system integrating multi-source environmental features according to claim 6, characterized in that, The preset qualification screening requirements include: The path search iteration count has reached the iteration count threshold; The number of qualified routes in the initial route database has reached the threshold for the number of qualified routes; The search unit library does not contain a path search unit.

8. A test route mapping system integrating multi-source environmental features according to claim 1, characterized in that, The optimal route selection module includes: A route extraction unit is configured to select several qualified routes with the smallest cumulative altitude deviation from the initial route database and obtain the average driving speed of each route. An altitude average deviation feature calculation unit is configured to calculate the altitude average deviation feature of all selected qualified routes. A speed deviation feature calculation unit is configured to calculate the speed deviation feature of each qualified route based on the average driving speed. The scoring calculation unit is configured to calculate a score based on the speed deviation characteristics and the average altitude deviation characteristics, and to select the route with the highest score to obtain the optimal test route for reproducing the target area.

9. A test route mapping system integrating multi-source environmental features according to claim 8, characterized in that, The scoring calculation unit is also configured to: The speed deviation characteristics and the average altitude deviation characteristics are normalized and weighted scoring is performed to obtain the optimal test route for reproducing the target area.

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