Area-aware data collection method and apparatus, medium, device
By analyzing the historical driving trajectories and target area ranges of data collection vehicles, and selecting appropriate vehicle combinations for area perception data collection, the problem of redundant data was solved, and efficient and low-cost data coverage was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA TELECOM CORP LTD TECHNOLOGY INNOVATION CENTER
- Filing Date
- 2023-10-10
- Publication Date
- 2026-05-19
AI Technical Summary
Existing methods for collecting sensory data result in excessive redundant data and high costs, and cannot effectively utilize the spatial correlation of sensory data for efficient collection.
By acquiring historical driving data of the current data collection vehicles, the historical driving trajectory is determined, and candidate vehicle combinations are selected based on the trajectory and target area range. The area is divided and filtered, and the target vehicle combinations are controlled to collect data, ensuring coverage of all space in the target area and reducing the number of vehicles.
While reducing the number of vehicles, it improved the comprehensiveness and effectiveness of regional perception data, reduced data redundancy, and lowered collection costs.
Smart Images
Figure CN117315937B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of big data processing technology, and more specifically, to a method for acquiring area-aware data, an apparatus for acquiring area-aware data, a computer-readable storage medium, and an electronic device. Background Technology
[0002] Existing methods for collecting sensory data involve increasing the number of people or vehicles involved in data collection to obtain sensory data covering a larger area.
[0003] However, since the sensing data has strong correlation in the spatial dimension, data from other sensing areas can be inferred by collecting data from some key nodes, without the need to collect data from the entire area. Therefore, the sensing data collected based on the existing sensing data collection methods has the problem of excessive redundant data.
[0004] It should be noted that the information in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0005] The purpose of this disclosure is to provide a method for acquiring area-aware data, an apparatus for acquiring area-aware data, a computer-readable storage medium, and an electronic device, thereby overcoming, to at least some extent, the problem of excessive redundant data caused by limitations and defects in related technologies.
[0006] According to one aspect of this disclosure, a method for acquiring area sensing data is provided, comprising:
[0007] Acquire the historical driving data of the current data collection vehicle, and determine the historical driving trajectory of the current data collection vehicle based on the historical driving data;
[0008] Based on the historical driving trajectory and the target area range of the target perception area, determine the current candidate vehicle corresponding to the target perception area, and construct a candidate vehicle combination based on the current candidate vehicle;
[0009] The target perception area is divided into multiple perception sub-regions, and the candidate vehicle combination is filtered based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain the target vehicle combination.
[0010] Control the target data acquisition vehicle included in the target vehicle group to collect area perception data of the target perception area.
[0011] In one exemplary embodiment of this disclosure, acquiring historical driving data of the current data acquisition vehicle and determining the historical driving trajectory of the current data acquisition vehicle based on the historical driving data includes:
[0012] The system acquires GPS data collected by the current data collection vehicle based on the GPS device, and parses the GPS data to obtain historical driving data.
[0013] The historical driving data is parsed to obtain the historical driving trajectory of the currently data-collecting vehicle; wherein, the historical driving trajectory includes multiple trajectory points.
[0014] In one exemplary embodiment of this disclosure, determining the current candidate vehicle corresponding to the target perception area based on the historical driving trajectory and the target area range of the target perception area includes:
[0015] Obtain the current position coordinates of the trajectory points included in the historical driving trajectory, and determine the target area range of the target perception area;
[0016] Determine whether the current position coordinates of the trajectory point are included within the target area;
[0017] If the current location coordinates are included within the target area, then the current data acquisition vehicle corresponding to the historical driving trajectory of the trajectory point including the current location coordinates is determined as the current candidate vehicle corresponding to the target perception area.
[0018] In one exemplary embodiment of this disclosure, constructing a candidate vehicle portfolio based on the current candidate vehicle includes:
[0019] The number of vehicles included in the candidate vehicle combination is determined based on the size of the target area range of the target perception area.
[0020] Select target candidate vehicles with the specified number of vehicles from the current candidate vehicles, and construct the candidate vehicle combination based on the target candidate vehicles; wherein, each target perception area corresponds to multiple candidate vehicle combinations.
[0021] In one exemplary embodiment of this disclosure, the candidate vehicle combination is filtered based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain a target vehicle combination, including:
[0022] Determine the sub-region coordinates of the perception sub-region and obtain the historical driving trajectories of the target candidate vehicles included in the candidate vehicle combination;
[0023] Based on the historical driving trajectory of the target candidate vehicle and the coordinates of the sub-region, the perception sub-regions traversed by the target candidate vehicles included in the candidate vehicle combination are determined.
[0024] The candidate vehicle combinations are filtered based on the sensing sub-regions traversed by the target candidate vehicles included in the candidate vehicle combinations, resulting in filtered candidate vehicle combinations.
[0025] The spatial correlation of the sensing sub-regions traversed by the filtered candidate vehicle combinations is calculated, and the filtered candidate vehicle combinations are further filtered based on the spatial correlation of the sensing sub-regions to obtain the target vehicle combinations.
[0026] In one exemplary embodiment of this disclosure, the candidate vehicle combination is filtered based on the sensing sub-region traversed by the target candidate vehicle included in the candidate vehicle combination to obtain a filtered candidate vehicle combination, including:
[0027] Determine the number of perception sub-regions traversed by the target candidate vehicle included in the candidate vehicle combination, and determine whether the number of regions is greater than the preset number of regions.
[0028] When the number of regions in the sensing sub-region is less than the preset number of regions, the candidate vehicle combinations corresponding to that number of regions are deleted, and the filtered candidate vehicle combinations are obtained based on the remaining candidate vehicle combinations after deletion.
[0029] In one exemplary embodiment of this disclosure, calculating the spatial correlation of the sensing sub-regions traversed by the filtered candidate vehicle combinations includes:
[0030] Obtain the sub-region coordinates of the sensing sub-regions traversed by the filtered candidate vehicle combinations, and calculate the current region distance between the sensing sub-regions based on the sub-region coordinates;
[0031] The current region distance is standardized to obtain a standard region distance, and the subspace correlation between the perceived sub-regions is determined based on the standard region distance.
[0032] Based on the subspace correlation, the spatial correlation of the sensing sub-regions traversed by the selected candidate vehicle combinations is determined.
[0033] In one exemplary embodiment of this disclosure, the current region distance is standardized to obtain a standard region distance, including:
[0034] Sort the current region distances and determine the maximum region distance from the current region distances based on the sorting results;
[0035] The standard area distance is obtained by calculating the ratio between the current area distance and the maximum area distance.
[0036] In one exemplary embodiment of this disclosure, the candidate vehicle combination is further filtered based on the spatial correlation of the perceived sub-region to obtain the target vehicle combination, including:
[0037] Based on the spatial correlation of the perceived sub-regions, the filtered candidate vehicle combinations are sorted to obtain the vehicle combination sorting result.
[0038] Based on the vehicle combination ranking results, the candidate vehicle combination with the least spatial correlation is selected from the candidate vehicle combinations as the target vehicle combination.
[0039] According to one aspect of this disclosure, a device for acquiring area sensing data is provided, comprising:
[0040] The historical driving trajectory determination module is used to acquire the historical driving data of the current data collection vehicle and determine the historical driving trajectory of the current data collection vehicle based on the historical driving data.
[0041] The current candidate vehicle determination module is used to determine the current candidate vehicle corresponding to the target perception area based on the historical driving trajectory and the target area range of the target perception area, and to construct a candidate vehicle combination based on the current candidate vehicle.
[0042] The candidate vehicle combination filtering module is used to divide the target perception area into multiple perception sub-regions, and filter the candidate vehicle combination based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain the target vehicle combination.
[0043] The area perception data acquisition module is used to control the target data acquisition vehicle included in the target vehicle combination to acquire area perception data of the target perception area.
[0044] According to one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the area-aware data acquisition method described in any of the preceding claims.
[0045] According to one aspect of this disclosure, an electronic device is provided, comprising:
[0046] Processor; and
[0047] Memory for storing the executable instructions of the processor;
[0048] The processor is configured to execute the area-aware data acquisition method described above by executing the executable instructions.
[0049] This disclosure provides a method for collecting area perception data. Firstly, it acquires historical driving data of the current data collection vehicle and determines its historical driving trajectory based on this data. Then, based on the historical driving trajectory and the target area range of the target perception area, it determines current candidate vehicles corresponding to the target perception area and constructs candidate vehicle combinations. Next, it divides the target perception area into multiple perception sub-regions and filters the candidate vehicle combinations based on the perception sub-regions traversed by the current candidate vehicles included in the candidate vehicle combinations, obtaining a target vehicle combination. Finally, it controls the target data collection vehicles included in the target vehicle combination to collect area perception data of the target perception area. Since the area is determined... During the data acquisition process of the domain perception data vehicle, the vehicle's trajectory space and the perception sub-region space are taken into account. This allows the regional distribution of the acquired data to cover the entire target perception area, thereby improving the comprehensiveness of the acquired regional perception data while reducing the number of data acquisition vehicles in the target perception area. This solves the problem of excessive redundant data in existing technologies. On the other hand, by filtering candidate vehicle combinations based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination, a target vehicle combination is obtained. Finally, the target data acquisition vehicles included in the target vehicle combination are controlled to acquire regional perception data in the target perception area, thereby improving the effectiveness of the regional perception data acquired by the target data acquisition vehicles.
[0050] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0052] Figure 1 The flowchart illustrates a method for acquiring area-aware data according to an exemplary embodiment of the present disclosure.
[0053] Figure 2 This diagram schematically illustrates an example of a historical driving trajectory including multiple trajectory points according to an exemplary embodiment of the present disclosure.
[0054] Figure 3 The illustration shows a scenario example of dividing a target sensing region into multiple sensing sub-regions according to an exemplary embodiment of the present disclosure.
[0055] Figure 4 The illustration shows a scenario example of dividing a target sensing region into multiple sensing sub-regions according to another example embodiment of the present disclosure.
[0056] Figure 5 The flowchart illustrates a method for filtering a candidate vehicle combination to obtain a target vehicle combination based on a sensing sub-region traversed by a current candidate vehicle included in a candidate vehicle combination, according to an exemplary embodiment of the present disclosure.
[0057] Figure 6 The diagram illustrates a scenario example of a region-aware data acquisition process according to an exemplary embodiment of the present disclosure.
[0058] Figure 7 The diagram illustrates a scenario example of an existing data acquisition method.
[0059] Figure 8 The illustration shows a scenario example of a data acquisition method proposed in this disclosure according to an example embodiment of this disclosure.
[0060] Figure 9 A block diagram schematically illustrates a region-aware data acquisition device according to an exemplary embodiment of the present disclosure.
[0061] Figure 10 An electronic device for implementing the above-described method for acquiring area-aware data is illustrated according to an example embodiment of the present disclosure. Detailed Implementation
[0062] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.
[0063] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0064] Traditional mobile crowdsourcing sensing applications typically acquire sensing data over a wider spatial coverage area by increasing the number of personnel and vehicles collecting data; however, this method significantly increases data acquisition costs. In reality, regional sensing data, such as environmental or traffic flow data, exhibits strong spatial correlations. Data collection from key nodes can infer data for other sensing areas, eliminating the need to collect data for the entire region. In other words, by collecting sensing data from key nodes within a target sensing area, regional sensing data for adjacent nodes can be inferred from the collected data. This approach improves the efficiency of regional sensing data collection while reducing its cost.
[0065] However, how to identify the key nodes in the target perception area and how to collect the area perception data of the key nodes are problems that urgently need to be solved.
[0066] Based on this, this exemplary embodiment first provides a method for collecting area-aware data. This method can run on a server, server cluster, or cloud server, etc. Of course, those skilled in the art can also run the method disclosed herein on other platforms as needed, and this exemplary embodiment does not impose any special limitations on this. Specifically, refer to... Figure 1 As shown, the method for collecting sensing data in this area may include the following steps:
[0067] Step S110. Obtain the historical driving data of the current data collection vehicle, and determine the historical driving trajectory of the current data collection vehicle based on the historical driving data;
[0068] Step S120. Based on the historical driving trajectory and the target area range of the target perception area, determine the current candidate vehicle corresponding to the target perception area, and construct a candidate vehicle combination based on the current candidate vehicle;
[0069] Step S130. Divide the target perception area into multiple perception sub-regions, and filter the candidate vehicle combination based on the perception sub-regions passed by the current candidate vehicle included in the candidate vehicle combination to obtain the target vehicle combination.
[0070] Step S140. Control the target data acquisition vehicle included in the target vehicle group to collect area perception data of the target perception area.
[0071] In the aforementioned method for collecting area perception data, on the one hand, historical driving data of the current data collection vehicle is acquired, and the historical driving trajectory of the current data collection vehicle is determined based on the historical driving data; then, based on the historical driving trajectory and the target area range of the target perception area, the current candidate vehicles corresponding to the target perception area are determined, and candidate vehicle combinations are constructed based on the current candidate vehicles; then, the target perception area is divided into multiple perception sub-regions, and the candidate vehicle combinations are filtered based on the perception sub-regions traversed by the current candidate vehicles included in the candidate vehicle combinations to obtain the target vehicle combination; finally, the target data collection vehicles included in the target vehicle combination are controlled to collect area perception data of the target perception area. Since the determination of area perception data... During the data acquisition process, the vehicle's trajectory space and the perception sub-region space are considered, thus enabling the regional distribution of the acquired data to cover the entire target perception area. This achieves the goal of improving the comprehensiveness of the acquired regional perception data while reducing the number of data acquisition vehicles in the target perception area, thereby solving the problem of excessive redundant data in existing technologies. On the other hand, by filtering candidate vehicle combinations based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination, a target vehicle combination is obtained. Finally, the target data acquisition vehicles included in the target vehicle combination are controlled to collect regional perception data in the target perception area, thereby improving the effectiveness of the regional perception data collected by the target data acquisition vehicles.
[0072] The method for collecting area-aware data described in the exemplary embodiments of this disclosure will be explained and described in detail below with reference to the accompanying drawings.
[0073] First, the proper nouns used in the exemplary embodiments of this disclosure will be explained.
[0074] Spatial correlation: Spatial correlation refers to the correlation between two different geographical regions in a spatial dimension. The closer the two geographical regions are, the stronger their spatial correlation, and the higher the similarity of the regional perception data collected by the sensors. For example, regarding air quality, if two geographical regions are only 100m apart, the collected air quality values will be roughly the same. In practical applications, if two different geographical regions are close to each other (i.e., have strong spatial correlation), the regional perception data of the two geographical regions will be roughly similar; in this scenario, a large amount of redundant data will exist.
[0075] Mobile crowd sensing: Mobile crowd sensing is a new type of data collection method that can utilize the mobility of people or vehicles to collect large-scale data more efficiently and quickly by using sensors carried by people or vehicles. The method for collecting regional sensing data described in the example embodiments of this disclosure mainly considers deploying various sensors on vehicles to collect regional sensing data of different areas in the city, such as environmental data and traffic data, by utilizing the mobility of vehicles.
[0076] Secondly, the technical implementation principle of the exemplary embodiments of this disclosure will be explained and described. Specifically, the method for collecting regional sensing data described in the exemplary embodiments of this disclosure can select the target data collection vehicle needed to collect regional sensing data in the target sensing area based on simultaneously considering the spatial coverage of sensing data and the spatial correlation between sensing data. Then, sensors are deployed on the target data collection vehicle to collect sensing data from only a portion of the nodes, which can complete the collection of sensing data at a lower cost and reduce the redundancy of the collected data.
[0077] The following will be about Figure 1 The method for acquiring area-aware data shown will be further explained and illustrated. Specifically:
[0078] In step S110, the historical driving data of the current data collection vehicle is acquired, and the historical driving trajectory of the current data collection vehicle is determined based on the historical driving data.
[0079] Specifically, acquiring the historical driving data of the current data collection vehicle and determining its historical driving trajectory based on this data can be achieved as follows: First, acquire Global Positioning System (GPS) data collected by the current data collection vehicle using a GPS device, and parse this data to obtain historical driving data; second, parse this historical driving data to obtain the historical driving trajectory of the current data collection vehicle; wherein the historical driving trajectory includes multiple trajectory points. That is, in practical applications, a GPS device can be configured on the current data collection vehicle (e.g., a bus or taxi, or other types of vehicles; this example does not impose any special restrictions). GPS data can be collected based on this device, and historical driving data can then be obtained from this GPS data. The historical driving data recorded here refers to the trajectory data generated by the current data collection vehicle while driving within the corresponding sensing area, which records the vehicle's position at different points in time.
[0080] Furthermore, after obtaining historical driving data, the historical driving trajectory can be obtained based on this data; the obtained historical driving trajectory can, for example, refer to... Figure 2 Among them, in Figure 2 The historical driving trajectory shown may include multiple trajectory points; these trajectory points may be, for example, bus stops, key intersections, buildings, tourist attractions, etc. This example does not impose any special restrictions on them.
[0081] In step S120, based on the historical driving trajectory and the target area range of the target perception area, the current candidate vehicle corresponding to the target perception area is determined, and a candidate vehicle combination is constructed based on the current candidate vehicle.
[0082] In this example embodiment, firstly, based on the historical driving trajectory and the target area range of the target perception area, the current candidate vehicle corresponding to the target perception area is determined. Specifically, this can be achieved as follows: First, obtain the current position coordinates of the trajectory points included in the historical driving trajectory and determine the target area range of the target perception area; secondly, determine whether the current position coordinates of the trajectory points are included within the target area range; then, if the current position coordinates are included within the target area range, determine the current data collection vehicle corresponding to the historical driving trajectory of the trajectory points including the current position coordinates as the current candidate vehicle corresponding to the target perception area. That is, in practical applications, the current candidate vehicles for area perception data collection can be initially screened based on whether the current coordinate positions of each trajectory point included in the historical driving trajectory are included within the target area range of the target perception area.
[0083] In one example embodiment, if the number of current candidate vehicles is large, the selected current candidate vehicles can be further filtered. For example, current candidate vehicles corresponding to historical driving trajectories with a small number of trajectory points included in the target perception area can be filtered out. For instance, if only one trajectory point in a historical driving trajectory is included within the target area of the target perception area, the current candidate vehicle corresponding to that historical driving trajectory can be filtered out; or, if only two trajectory points in a historical driving trajectory are included within the target area of the target perception area, the current candidate vehicle corresponding to that historical trajectory can also be filtered out. In practical applications, the specific selection method for current candidate vehicles can be chosen according to actual needs, and this example does not impose any special restrictions on this.
[0084] Secondly, candidate vehicle combinations are constructed based on the current candidate vehicles. Specifically, this can be achieved as follows: First, the number of vehicles included in the candidate vehicle combination is determined based on the size of the target area of the target perception region. Second, target candidate vehicles with the specified number of vehicles are extracted from the current candidate vehicles, and the candidate vehicle combination is constructed based on these target candidate vehicles. Each target perception region corresponds to multiple candidate vehicle combinations. Specifically, in practical applications, the number of vehicles included in each candidate vehicle combination can be determined based on the size of the target area of the target perception region. This number of vehicles can be determined based on a corresponding vehicle quantity prediction model. Then, once the number of vehicles is determined, a random combination method can be used to extract the corresponding number of target candidate vehicles from the current candidate vehicle axis to form the candidate vehicle combination. The principle to be followed during the extraction of target candidate vehicles is that the resulting candidate vehicle combination must include all target candidate vehicles. This avoids the problem of not being able to select the optimal target data collection vehicle due to incomplete coverage of all target candidate vehicles, thus preventing low accuracy of the collected area perception data.
[0085] For example, suppose that for cost reasons, P vehicles are available for data collection, and BS is the set of possible vehicle combinations. k It is one of the vehicle combination methods, BS k A total of P vehicles are included; and the resulting candidate vehicle combination BS can be shown as follows:
[0086] BS = {BS1, BS2, ..., BS} k}
[0087] In step S130, the target perception area is divided into multiple perception sub-regions, and the candidate vehicle combination is filtered based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain the target vehicle combination.
[0088] In this example embodiment, the target sensing region is first divided into multiple sensing sub-regions. The division of the target sensing region can be implemented in the following ways: one is by dividing it according to a fixed-size grid; for example, by setting a step size and dividing sequentially within the target area of the target sensing region, the following sub-regions can be obtained: Figure 3 The sensory sub-regions shown are approximately the same size; for example, they can be divided into units based on the streets included within the target sensory region to obtain the following: Figure 4The example shows multiple sensing sub-regions; the size of each sensing sub-region can be determined according to the actual situation of the street; it can be a horizontal street 401 or a vertical street 402, and this example does not impose any special restrictions on it; of course, in actual applications, it can also be divided based on other methods, such as by buildings, communities, etc., and this example does not impose any special restrictions on it.
[0089] Secondly, based on the perception sub-regions traversed by the current candidate vehicles included in the candidate vehicle combinations, the candidate vehicle combinations are filtered to obtain the target vehicle combinations; specifically, refer to... Figure 5 As shown, the following steps may be included:
[0090] Step S510: Determine the sub-region coordinates of the sensing sub-region and obtain the historical driving trajectories of the target candidate vehicles included in the candidate vehicle combination.
[0091] Specifically, the sub-region coordinates of the sensing sub-region recorded here can be determined based on the center coordinate point of the sensing sub-region; that is, first determine the center coordinates of the sensing sub-region, and then use the center coordinates as the sub-region coordinates; at the same time, the historical driving trajectory of the target candidate vehicle recorded here can be determined based on the historical driving data of the target candidate vehicle.
[0092] Step S520: Based on the historical driving trajectory of the target candidate vehicle and the coordinates of the sub-region, determine the perception sub-region traversed by the target candidate vehicle included in the candidate vehicle combination.
[0093] Specifically, the current position coordinates of trajectory points in the historical driving trajectory of the target candidate vehicle can be obtained, and it can be determined whether the current position coordinates and the sub-region coordinates overlap. If they overlap, it is determined that the target candidate vehicle included in the candidate vehicle combination has passed through the sensing sub-region; if they do not overlap, it is determined that the target candidate vehicle included in the candidate vehicle combination has not passed through the sensing sub-region. Simultaneously, an error interval can be set during the process of determining whether the current position coordinates and the sub-region coordinates overlap. That is, if the distance difference between the current position coordinates and the sub-region coordinates is within the error interval, it is determined that the current position coordinates and the sub-region coordinates overlap; otherwise, they do not overlap. Furthermore, the size of this error interval can be determined according to actual needs; this example does not impose any special restrictions on it. For example, based on the historical driving trajectory of the target candidate vehicle, the sensing sub-region Traj_BS traversed by the target candidate vehicle included in each candidate vehicle combination can be obtained. k The set is: Traj_BS k ={l1,l2,l3,…,l n}; where l1, l2, l3, ..., l nThis represents the number of perceptual sub-regions traversed.
[0094] Step S530: The candidate vehicle combination is filtered according to the perception sub-regions traversed by the target candidate vehicle included in the candidate vehicle combination to obtain the filtered candidate vehicle combination.
[0095] Specifically, the candidate vehicle combinations are filtered based on the sensing sub-regions traversed by the target candidate vehicles included in the candidate vehicle combinations, resulting in filtered candidate vehicle combinations. This can be achieved as follows: First, determine the number of sensing sub-regions traversed by the target candidate vehicles included in the candidate vehicle combinations, and check if this number is greater than a preset number of regions. Second, if the number of sensing sub-regions is less than the preset number of regions, delete the candidate vehicle combinations corresponding to that number of regions, and obtain the filtered candidate vehicle combinations based on the remaining candidate vehicle combinations after deletion. That is, in practical applications, the total number of sensing regions traversed by the target candidate vehicles included in the candidate vehicle combinations (SC_BS) can be counted first. k For example: vehicle combination BS k It passed through a total of 3 perceptual sub-regions, then SC_BS k =3; Meanwhile, since the spatial coverage of candidate vehicle combinations needs to be greater than a certain spatial range threshold (preset number of areas) SC, to avoid selecting vehicle combinations with very small spatial coverage, it is necessary to determine whether the number of areas is greater than the preset number of areas SC; that is, only when SC_BS k In the case of >SC, the candidate vehicle combination can be retained; otherwise, it needs to be eliminated.
[0096] Step S540: Calculate the spatial correlation of the sensing sub-regions traversed by the filtered candidate vehicle combinations, and further filter the filtered candidate vehicle combinations based on the spatial correlation of the sensing sub-regions to obtain the target vehicle combinations.
[0097] In this example embodiment, firstly, the spatial correlation of the sensing sub-regions traversed by the filtered candidate vehicle combination is calculated. Specifically, this can be achieved as follows: First, the coordinates of the sub-regions traversed by the filtered candidate vehicle combination are obtained, and the current area distance between the sensing sub-regions is calculated based on the sub-region coordinates; secondly, the current area distance is standardized to obtain a standard area distance, and the sub-spatial correlation between the sensing sub-regions is determined based on the standard area distance; then, the spatial correlation of the sensing sub-regions traversed by the filtered candidate vehicle combination is determined based on the sub-spatial correlation.
[0098] In one example embodiment, the current region distance is standardized to obtain a standard region distance, which can be achieved as follows: First, the current region distances are sorted, and the maximum region distance is determined from the current region distances based on the sorting results; second, the ratio between the current region distance and the maximum region distance is calculated to obtain the standard region distance.
[0099] The following section will further explain and illustrate the specific calculation process of the spatial correlation of the sensing sub-regions traversed by the selected candidate vehicle combinations. Specifically, it is assumed that the entire target sensing area is divided into L sensing sub-regions, and the current distance between any two sensing sub-regions i and j is D. ij The specific calculation process for the current region distance is as follows: Calculate the coordinate difference between the current region coordinates of the sensing sub-regions, and then obtain the current region distance based on this coordinate difference; then, the region standardizes the distance to obtain the standard region distance; the specific calculation process for the standard region distance between sensing sub-regions i and j is as follows:
[0100] Among them, ND i,j D is the standard area distance. i,j The current area distance is given by Max(D), and the maximum area distance is given by Max(D). This maximum area distance can be the maximum current area distance between the sensing sub-regions traversed by a candidate vehicle, or it can be the maximum current area distance between all sensing sub-regions. This example does not impose any special restrictions on this. It should be noted that the smaller the standard distance, the greater the correlation between the two sensing sub-regions.
[0101] Furthermore, after obtaining the standard region distance, the subspace correlation between two perceptual sub-intervals can be obtained; for example, the correlation between any two perceptual regions i and j can be defined as S. i,j Then we have: S i,j =1-ND i,j .
[0102] Furthermore, after obtaining the sub-correlation between the perceived sub-regions, the sub-correlation can be summed to obtain the spatial correlation (Similarity_BS) of the perceived sub-regions traversed by the filtered candidate vehicle combinations. k Specifically, Similarity_BS k The specific calculation process is as follows:
[0103]
[0104] Secondly, based on the spatial correlation of the perceived sub-region, the selected candidate vehicle combinations are further filtered to obtain the target vehicle group. Specifically, this can be achieved as follows: First, the selected candidate vehicle combinations are sorted according to the spatial correlation of the perceived sub-region to obtain a vehicle combination sorting result; second, based on the vehicle combination sorting result, the candidate vehicle combination with the lowest spatial correlation is selected as the target vehicle group. That is, the target vehicle group can be selected based on the spatial correlation (Similarity_BS) of different filtered candidate vehicle combinations. k The candidate vehicle combinations are sorted after screening, and the combination with the lowest spatial correlation is selected, i.e., Minimize(Similarity_BS). k () as the target vehicle combination.
[0105] In step S140, the target data acquisition vehicle included in the target vehicle group is controlled to collect area perception data of the target perception area.
[0106] Furthermore, after determining the target vehicle combination, sensors can be deployed in the target data acquisition vehicles included in the target vehicle combination, and then the sensors can be controlled to collect regional perception data. In this way, it can be ensured that the path traveled by the target data acquisition vehicles has the characteristics of large spatial coverage and low spatial correlation, and that the regional perception data collected by the target data acquisition vehicles has the characteristics of relatively dispersed location, thereby ensuring that the collected regional perception data is not prone to data redundancy.
[0107] The following section will further explain and illustrate the specific implementation process of the data collection method for this area, using concrete example diagrams. For details, please refer to... Figure 6 As shown, taking a taxi as an example, the area perception data collected, starting from sub-sensing area 601 and ending at sub-sensing area 602, can cover multiple different sub-sensing areas; further, refer to... Figure 7 as well as Figure 8 As shown, data is also collected from four sub-sensing regions. Figure 7 The data acquisition method shown involves four regions that are relatively close to each other and have high spatial correlation, which may lead to data redundancy; however... Figure 8 The data collected using the data acquisition method described in this example embodiment is geographically dispersed and has low spatial correlation. Therefore, the regional sensing data collected by the data acquisition method described in this example embodiment is more effective.
[0108] Thus, the method for collecting area perception data described in the exemplary embodiments of this disclosure has been fully implemented. Based on the foregoing description, it can be understood that the method for collecting area perception data described in the exemplary embodiments of this disclosure can consider the spatial coverage of vehicle trajectories and the spatial correlation between data collection areas. Based on this, the selection of vehicles participating in the data collection task can ensure that the distribution of collected data areas is as dispersed as possible, reducing data redundancy and improving data effectiveness.
[0109] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.
[0110] This disclosure also provides an example embodiment of a device for acquiring area-aware data. Specifically, refer to... Figure 9 As shown, the area perception data acquisition device may include a historical driving trajectory determination module 910, a current candidate vehicle determination module 920, a candidate vehicle combination screening module 930, and an area perception data acquisition module 940. Wherein:
[0111] The historical driving trajectory determination module 910 can be used to acquire the historical driving data of the current data collection vehicle and determine the historical driving trajectory of the current data collection vehicle based on the historical driving data.
[0112] The current candidate vehicle determination module 920 can be used to determine the current candidate vehicle corresponding to the target perception area based on the historical driving trajectory and the target area range of the target perception area, and to construct a candidate vehicle combination based on the current candidate vehicle.
[0113] The candidate vehicle combination filtering module 930 can be used to divide the target perception area into multiple perception sub-regions, and filter the candidate vehicle combination based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain the target vehicle combination.
[0114] The area perception data acquisition module 940 can be used to control the target data acquisition vehicle included in the target vehicle combination to acquire area perception data of the target perception area.
[0115] In one exemplary embodiment of this disclosure, acquiring historical driving data of the current data acquisition vehicle and determining the historical driving trajectory of the current data acquisition vehicle based on the historical driving data includes: acquiring global positioning system data collected by the current data acquisition vehicle based on a global positioning system device, and parsing the global positioning system data to obtain historical driving data; parsing the historical driving data to obtain the historical driving trajectory of the current data acquisition vehicle; wherein the historical driving trajectory includes multiple trajectory points.
[0116] In an exemplary embodiment of this disclosure, determining the current candidate vehicle corresponding to the target perception area based on the historical driving trajectory and the target area range of the target perception area includes: obtaining the current position coordinates of trajectory points included in the historical driving trajectory and determining the target area range of the target perception area; determining whether the current position coordinates of the trajectory points are included in the target area range; if the current position coordinates are included in the target area range, then determining the current data acquisition vehicle corresponding to the historical driving trajectory of the trajectory points including the current position coordinates as the current candidate vehicle corresponding to the target perception area.
[0117] In one exemplary embodiment of this disclosure, constructing a candidate vehicle combination based on the current candidate vehicles includes: determining the number of vehicles included in the candidate vehicle combination based on the size of the target area range of the target perception area; extracting target candidate vehicles with the number of vehicles from the current candidate vehicles, and constructing the candidate vehicle combination based on the target candidate vehicles; wherein each target perception area corresponds to multiple candidate vehicle combinations.
[0118] In one exemplary embodiment of this disclosure, filtering the candidate vehicle combination based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain a target vehicle combination includes: determining the sub-region coordinates of the perception sub-regions and obtaining the historical driving trajectories of the target candidate vehicles included in the candidate vehicle combination; determining the perception sub-regions traversed by the target candidate vehicles included in the candidate vehicle combination based on the historical driving trajectories of the target candidate vehicles and the sub-region coordinates; filtering the candidate vehicle combination according to the perception sub-regions traversed by the target candidate vehicles included in the candidate vehicle combination to obtain a filtered candidate vehicle combination; calculating the spatial correlation of the perception sub-regions traversed by the filtered candidate vehicle combination, and further filtering the filtered candidate vehicle combination according to the spatial correlation of the perception sub-regions to obtain the target vehicle combination.
[0119] In one exemplary embodiment of this disclosure, filtering the candidate vehicle combination based on the sensing sub-regions traversed by the target candidate vehicle included in the candidate vehicle combination to obtain a filtered candidate vehicle combination includes: determining the number of sensing sub-regions traversed by the target candidate vehicle included in the candidate vehicle combination, and determining whether the number of regions is greater than a preset number of regions; when it is determined that the number of sensing sub-regions is less than the preset number of regions, deleting the candidate vehicle combination corresponding to the number of regions, and obtaining the filtered candidate vehicle combination based on the remaining candidate vehicle combinations after deletion.
[0120] In one exemplary embodiment of this disclosure, calculating the spatial correlation of the sensing sub-regions traversed by the filtered candidate vehicle combination includes: obtaining the sub-region coordinates of the sensing sub-regions traversed by the filtered candidate vehicle combination, and calculating the current region distance between the sensing sub-regions based on the sub-region coordinates; standardizing the current region distance to obtain a standard region distance, and determining the sub-spatial correlation between the sensing sub-regions based on the standard region distance; and determining the spatial correlation of the sensing sub-regions traversed by the filtered candidate vehicle combination based on the sub-spatial correlation.
[0121] In one exemplary embodiment of this disclosure, standardizing the current region distance to obtain a standard region distance includes: sorting the current region distances and determining the maximum region distance from the current region distances based on the region distance sorting results; and calculating the ratio between the current region distance and the maximum region distance to obtain the standard region distance.
[0122] In an exemplary embodiment of this disclosure, the process of further filtering the selected candidate vehicle combinations based on the spatial correlation of the sensing sub-region to obtain a target vehicle combination includes: sorting the selected candidate vehicle combinations according to the spatial correlation of the sensing sub-region to obtain a vehicle combination sorting result; and selecting the candidate vehicle combination with the lowest spatial correlation from the candidate vehicle combinations based on the vehicle combination sorting result as the target vehicle combination.
[0123] The specific details of each module in the aforementioned area sensing data acquisition device have been described in detail in the corresponding area sensing data acquisition method, so they will not be repeated here.
[0124] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.
[0125] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.
[0126] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.
[0127] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."
[0128] The following reference Figure 10 To describe an electronic device 1000 according to such an embodiment of the present disclosure. Figure 10 The electronic device 1000 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0129] like Figure 10 As shown, the electronic device 1000 is manifested in the form of a general-purpose computing device. The components of the electronic device 1000 may include, but are not limited to: at least one processing unit 1010, at least one storage unit 1020, a bus 1030 connecting different system components (including storage unit 1020 and processing unit 1010), and a display unit 1040.
[0130] The storage unit stores program code that can be executed by the processing unit 1010, causing the processing unit 1010 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 1010 can perform actions such as... Figure 1Step S110: Obtain historical driving data of the current data acquisition vehicle, and determine the historical driving trajectory of the current data acquisition vehicle based on the historical driving data; Step S120: Based on the historical driving trajectory and the target area range of the target perception area, determine the current candidate vehicle corresponding to the target perception area, and construct a candidate vehicle combination based on the current candidate vehicle; Step S130: Divide the target perception area into multiple perception sub-regions, and filter the candidate vehicle combination based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain a target vehicle combination; Step S140: Control the target data acquisition vehicle included in the target vehicle combination to collect the area perception data of the target perception area.
[0131] Storage unit 1020 may include readable media in the form of volatile storage units, such as random access memory (RAM) 10201 and / or cache memory 10202, and may further include read-only memory (ROM) 10203.
[0132] Storage unit 1020 may also include a program / utility 10204 having a set (at least one) program module 10205, such program module 10205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.
[0133] Bus 1030 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.
[0134] Electronic device 1000 can also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 1000, and / or any device that enables electronic device 1000 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 1050. Furthermore, electronic device 1000 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 1060. As shown, network adapter 1060 communicates with other modules of electronic device 1000 via bus 1030. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0135] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.
[0136] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this disclosure described in the "Exemplary Methods" section above.
[0137] The program product for implementing the above-described method according to embodiments of the present disclosure may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.
[0138] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0139] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.
[0140] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0141] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0142] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.
[0143] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention described herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not invented by this disclosure. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.
Claims
1. A method for collecting area sensing data, characterized in that, include: Acquire the historical driving data of the current data collection vehicle, and determine the historical driving trajectory of the current data collection vehicle based on the historical driving data; Based on the historical driving trajectory and the target area range of the target perception area, determine the current candidate vehicle corresponding to the target perception area, and construct a candidate vehicle combination based on the current candidate vehicle; The target perception area is divided into multiple perception sub-regions, and the candidate vehicle combination is filtered based on the perception sub-regions traversed by the current candidate vehicle included in the candidate vehicle combination to obtain the target vehicle combination. This includes: determining the sub-region coordinates of the perception sub-regions and obtaining the historical driving trajectories of the target candidate vehicles included in the candidate vehicle combination. Based on the historical driving trajectory of the target candidate vehicle and the coordinates of the sub-region, the perception sub-regions traversed by the target candidate vehicles included in the candidate vehicle combination are determined; the candidate vehicle combination is filtered according to the perception sub-regions traversed by the target candidate vehicles included in the candidate vehicle combination to obtain the filtered candidate vehicle combination; the spatial correlation of the perception sub-regions traversed by the filtered candidate vehicle combination is calculated, and the filtered candidate vehicle combination is filtered again according to the spatial correlation of the perception sub-regions to obtain the target vehicle combination. Control the target data acquisition vehicle included in the target vehicle group to collect area perception data of the target perception area.
2. The method for acquiring area sensing data according to claim 1, characterized in that, Acquire historical driving data of the current data collection vehicle, and determine the historical driving trajectory of the current data collection vehicle based on the historical driving data, including: The system acquires GPS data collected by the current data collection vehicle based on the GPS device, and parses the GPS data to obtain historical driving data. The historical driving data is parsed to obtain the historical driving trajectory of the currently data-collecting vehicle; wherein, the historical driving trajectory includes multiple trajectory points.
3. The method for acquiring area sensing data according to claim 1, characterized in that, Based on the historical driving trajectory and the target area range of the target perception area, determine the current candidate vehicle corresponding to the target perception area, including: Obtain the current position coordinates of the trajectory points included in the historical driving trajectory, and determine the target area range of the target perception area; Determine whether the current position coordinates of the trajectory point are included within the target area; If the current location coordinates are included within the target area, then the current data acquisition vehicle corresponding to the historical driving trajectory of the trajectory point including the current location coordinates is determined as the current candidate vehicle corresponding to the target perception area.
4. The method for acquiring area sensing data according to claim 1, characterized in that, Constructing candidate vehicle combinations based on the current candidate vehicles includes: The number of vehicles included in the candidate vehicle combination is determined based on the size of the target area range of the target perception area. Select target candidate vehicles with the specified number of vehicles from the current candidate vehicles, and construct the candidate vehicle combination based on the target candidate vehicles; wherein, each target perception area corresponds to multiple candidate vehicle combinations.
5. The method for acquiring area sensing data according to claim 1, characterized in that, The candidate vehicle combinations are filtered based on the sensing sub-regions traversed by the target candidate vehicles included in the candidate vehicle combinations, resulting in filtered candidate vehicle combinations, including: Determine the number of perception sub-regions traversed by the target candidate vehicle included in the candidate vehicle combination, and determine whether the number of regions is greater than the preset number of regions. When the number of regions in the sensing sub-region is less than the preset number of regions, the candidate vehicle combinations corresponding to that number of regions are deleted, and the filtered candidate vehicle combinations are obtained based on the remaining candidate vehicle combinations after deletion.
6. The method for acquiring area sensing data according to claim 1, characterized in that, Calculate the spatial correlation of the perceived sub-regions traversed by the filtered candidate vehicle combinations, including: Obtain the sub-region coordinates of the sensing sub-regions traversed by the filtered candidate vehicle combinations, and calculate the current region distance between the sensing sub-regions based on the sub-region coordinates; The current region distance is standardized to obtain a standard region distance, and the subspace correlation between the perceived sub-regions is determined based on the standard region distance. Based on the subspace correlation, the spatial correlation of the sensing sub-regions traversed by the selected candidate vehicle combinations is determined.
7. The method for acquiring area sensing data according to claim 6, characterized in that, The current region distance is standardized to obtain the standard region distance, including: Sort the current region distances and determine the maximum region distance from the current region distances based on the sorting results; The standard area distance is obtained by calculating the ratio between the current area distance and the maximum area distance.
8. The method for acquiring area sensing data according to claim 1, characterized in that, Based on the spatial correlation of the perceived sub-regions, the candidate vehicle combinations are further filtered to obtain the target vehicle combinations, including: Based on the spatial correlation of the perceived sub-regions, the filtered candidate vehicle combinations are sorted to obtain the vehicle combination sorting result. Based on the vehicle combination ranking results, the candidate vehicle combination with the least spatial correlation is selected from the candidate vehicle combinations as the target vehicle combination.
9. A device for acquiring area sensing data, characterized in that, include: The historical driving trajectory determination module is used to acquire the historical driving data of the current data collection vehicle and determine the historical driving trajectory of the current data collection vehicle based on the historical driving data. The current candidate vehicle determination module is used to determine the current candidate vehicle corresponding to the target perception area based on the historical driving trajectory and the target area range of the target perception area, and to construct a candidate vehicle combination based on the current candidate vehicle. A candidate vehicle combination filtering module is used to divide the target perception area into multiple perception sub-regions, and filter the candidate vehicle combinations based on the perception sub-regions traversed by the current candidate vehicles included in the candidate vehicle combinations to obtain target vehicle combinations. The module includes: determining the sub-region coordinates of the perception sub-regions and obtaining the historical driving trajectories of the target candidate vehicles included in the candidate vehicle combinations; determining the perception sub-regions traversed by the target candidate vehicles included in the candidate vehicle combinations based on the historical driving trajectories of the target candidate vehicles and the sub-region coordinates; filtering the candidate vehicle combinations according to the perception sub-regions traversed by the target candidate vehicles included in the candidate vehicle combinations to obtain filtered candidate vehicle combinations; calculating the spatial correlation of the perception sub-regions traversed by the filtered candidate vehicle combinations, and further filtering the filtered candidate vehicle combinations based on the spatial correlation of the perception sub-regions to obtain target vehicle combinations. The area perception data acquisition module is used to control the target data acquisition vehicle included in the target vehicle combination to acquire area perception data of the target perception area.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for acquiring area-aware data as described in any one of claims 1-8.
11. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the area-aware data acquisition method according to any one of claims 1-8 by executing the executable instructions.