阅读说明：本技术 确定最近道路边界的方法、装置、设备和存储介质 (Method, apparatus, device and storage medium for determining nearest road boundary ) 是由 李亨通 顾天宇 于 2020-11-06 设计创作，主要内容包括：本公开涉及一种用于确定最近道路边界的方法、装置、设备和存储介质。在此描述的方法包括：获取目标对象的位置信息；获取表征道路边界的层级化数据结构,该层级化数据结构分层级地存储道路边界的多个分段的位置数据；以及基于位置信息和层级化数据结构,从道路边界的多个分段中确定目标分段,该目标分段是多个分段中与目标对象距离最近的分段。在本公开的实施例中,通过利用表征道路边界的层级化数据结构,能够提高确定与目标对象距离最近的道路边界分段的效率。(The present disclosure relates to a method, apparatus, device, and storage medium for determining a nearest road boundary. The method described herein comprises: acquiring position information of a target object; acquiring a hierarchical data structure representing a road boundary, wherein the hierarchical data structure stores position data of a plurality of segments of the road boundary in a hierarchical manner; and determining a target segment from the plurality of segments of the road boundary based on the location information and the hierarchical data structure, the target segment being a segment of the plurality of segments that is closest to the target object. In the embodiment of the disclosure, by using the hierarchical data structure for representing the road boundary, the efficiency of determining the road boundary segment closest to the target object can be improved.)

1. A method for determining a nearest road boundary, comprising:

acquiring position information of a target object;

obtaining a hierarchical data structure characterizing a road boundary, the hierarchical data structure storing location data of a plurality of segments of the road boundary hierarchically; and

determining a target segment from the plurality of segments of the road boundary based on the location information and the hierarchical data structure, the target segment being a closest segment of the plurality of segments to the target object.

2. The method of claim 1, wherein the location data indicates locations of endpoints of the plurality of segments.

3. The method of claim 2, wherein the location data is stored in a leaf node of the hierarchical data structure, a parent node of the leaf node storing boundary data, a segment defined by the location data in the leaf node being within a bounding box defined by the boundary data in the parent node.

4. The method of claim 3, wherein determining the target segment from the plurality of segments of the road boundary comprises:

obtaining boundary data in a plurality of parent nodes of the hierarchical data structure, the plurality of parent nodes being child nodes of a root node of the hierarchical data structure;

determining a first target parent node from the plurality of parent nodes based on boundary data in the plurality of parent nodes, a bounding box corresponding to the first target parent node being a closest bounding box to the target object among a plurality of bounding boxes corresponding to the plurality of parent nodes;

determining at least one target leaf node associated with the first target parent node; and

determining the target segment from at least one segment corresponding to the at least one target leaf node based on location data in the at least one target leaf node.

5. The method of claim 4, wherein determining at least one target leaf node associated with the first target parent node comprises:

acquiring boundary data in child nodes of the first target parent node;

determining a second target parent node from the children nodes of the first target parent node based on the boundary data in the children nodes of the first target parent node, the bounding box corresponding to the second target parent node being the closest bounding box to the target object among the plurality of bounding boxes corresponding to the children nodes of the first target parent node; and

determining a child node of the second target parent node as the at least one target leaf node if it is determined that the child node of the second target parent node is a leaf node.

6. The method of claim 4 or 5, wherein determining the target segment from at least one segment corresponding to the at least one target leaf node based on location data in the at least one target leaf node comprises:

determining a distance of the target object from at least one segment defined by location data in the at least one target leaf node; and

determining the target segment from the at least one segment based on a distance of the target object from the at least one segment.

7. The method of claim 1, further comprising:

performing a driving decision and/or a route planning based on a distance of the target object from the target segment.

8. An apparatus for determining a nearest road boundary, comprising:

a position information acquisition module configured to acquire position information of a target object;

a hierarchical data structure acquisition module configured to acquire a hierarchical data structure representing a road boundary, the hierarchical data structure hierarchically storing location data of a plurality of segments of the road boundary; and

a first target segment determination module configured to determine a target segment from the plurality of segments of the road boundary based on the location information and the hierarchical data structure, the target segment being a closest segment of the plurality of segments to the target object.

9. The apparatus of claim 8, wherein the location data indicates locations of endpoints of the plurality of segments.

10. The apparatus of claim 9, wherein the location data is stored in a leaf node of the hierarchical data structure, a parent node of the leaf node storing boundary data, a segment defined by the location data in the leaf node being within a bounding box defined by the boundary data in the parent node.

11. The apparatus of claim 10, wherein the first target segmentation determination module comprises:

a first boundary data acquisition module configured to acquire boundary data in a plurality of parent nodes of the hierarchical data structure, the plurality of parent nodes being child nodes of a root node of the hierarchical data structure;

a first target parent node determination module configured to determine a first target parent node from the plurality of parent nodes based on boundary data in the plurality of parent nodes, a bounding box corresponding to the first target parent node being a closest bounding box to the target object among a plurality of bounding boxes corresponding to the plurality of parent nodes;

a first leaf node determination module configured to determine at least one target leaf node associated with the first target parent node; and

a second target segment determination module configured to determine the target segment from at least one segment corresponding to the at least one target leaf node based on location data in the at least one target leaf node.

12. The apparatus of claim 11, wherein the first leaf node determination module comprises:

a second boundary data acquisition module configured to acquire boundary data in child nodes of the first target parent node;

a second target parent node determination module configured to determine a second target parent node from the child nodes of the first target parent node based on the boundary data in the child nodes of the first target parent node, a bounding box corresponding to the second target parent node being a closest bounding box to the target object among the bounding boxes corresponding to the child nodes of the first target parent node; and

a second leaf node determination module configured to: determining a child node of the second target parent node as the at least one target leaf node if it is determined that the child node of the second target parent node is a leaf node.

13. The apparatus of claim 11 or 12, wherein the second target segmentation determination module comprises:

a distance determination module configured to determine a distance of the target object from at least one segment defined by location data in the at least one target leaf node; and

a third target segmentation determination module configured to determine the target segmentation from the at least one segmentation based on a distance of the target object from the at least one segmentation.

14. The apparatus of claim 8, further comprising:

a decision and plan execution module configured to execute a driving decision and/or a route plan based on a distance of the target object from the target segment.

15. An electronic device, comprising:

a processor; and

a memory storing computer-executable instructions that, when executed by the processor, are configured to implement the method of any of claims 1 to 6.

16. A computer-readable storage medium having computer-executable instructions stored thereon, wherein the computer-executable instructions are executed by a processor to implement the method of any one of claims 1 to 6.

Technical Field

The present disclosure relates generally to the field of autonomous driving, and more particularly to a method, apparatus, device, and computer-readable storage medium for determining a nearest road boundary.

Background

With the development of technology, automatic driving of vehicles is achieved through cooperative cooperation of various technologies such as a global positioning system, visual computing, and artificial intelligence. The automatic driving refers to driving of a vehicle automatically controlled by a computer without any human active operation. In the field of autonomous driving, efficient and accurate determination of road boundaries is crucial for safety reasons.

Disclosure of Invention

According to some embodiments of the present disclosure, a method, an apparatus, a device, and a computer-readable storage medium for determining a nearest road boundary are provided.

In a first aspect of the disclosure, a method for determining a nearest road boundary is provided. The method comprises the following steps: acquiring position information of a target object; acquiring a hierarchical data structure representing a road boundary, wherein the hierarchical data structure stores position data of a plurality of segments of the road boundary in a hierarchical manner; and determining a target segment from the plurality of segments of the road boundary based on the location information and the hierarchical data structure, the target segment being a segment of the plurality of segments that is closest to the target object.

In a second aspect of the disclosure, an apparatus for determining a nearest road boundary is provided. The device comprises a position information acquisition module, a hierarchical data structure acquisition module and a first target segmentation determination module. The position information acquisition module is configured to acquire position information of the target object; the hierarchical data structure acquisition module is configured to acquire a hierarchical data structure representing a road boundary, the hierarchical data structure hierarchically storing location data of a plurality of segments of the road boundary; the first target segment determination module is configured to determine a target segment from a plurality of segments of the road boundary based on the location information and the hierarchical data structure, the target segment being a segment of the plurality of segments that is closest in distance to the target object.

In a third aspect of the present disclosure, there is provided an electronic device comprising a memory and a processor, wherein the memory is for storing computer-executable instructions that are executed by the processor to implement a method according to the first aspect of the present disclosure.

In a fourth aspect of the present disclosure, a computer-readable storage medium is provided having computer-executable instructions stored thereon, wherein the computer-executable instructions are executed by a processor to implement a method according to the first aspect and of the present disclosure.

In the embodiment of the disclosure, by using the hierarchical data structure for representing the road boundary, the efficiency of determining the road boundary segment closest to the target object can be improved.

Drawings

Features, advantages, and other aspects of various implementations of the disclosure will become more apparent with reference to the following detailed description when taken in conjunction with the accompanying drawings. Several implementations of the present disclosure are illustrated herein by way of example, and not by way of limitation, in the figures of the accompanying drawings:

FIG. 1 illustrates an example environment for determining a nearest road boundary in accordance with an embodiment of the present disclosure;

fig. 2 is a schematic diagram illustrating a method for determining a nearest road boundary in a conventional scheme;

FIG. 3 shows a flowchart of an example method for determining a nearest road boundary, in accordance with an embodiment of the present disclosure;

FIG. 4A shows a schematic diagram of road boundary characterization, according to an embodiment of the present disclosure;

FIG. 4B shows a schematic diagram of a hierarchical data structure characterizing a road boundary, according to an embodiment of the present disclosure;

FIG. 5 shows a flowchart of an example method for determining a target segment closest in distance to a target object in a road boundary, in accordance with an embodiment of the present disclosure;

FIG. 6 shows a block diagram of an apparatus for determining a nearest road boundary according to an embodiment of the present disclosure; and

FIG. 7 illustrates a block diagram of a computing device in which one or more embodiments of the disclosure may be implemented.

Detailed Description

Preferred implementations of the present disclosure will be described in more detail below with reference to the accompanying drawings. While a preferred implementation of the present disclosure is shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited by the implementations set forth herein. Rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "embodiment" and "some embodiments" mean "at least some embodiments". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As mentioned above, in the field of autonomous driving, it is crucial for safety reasons to efficiently and accurately determine the road boundary closest to a target object, such as an autonomous vehicle. For example, the computer should ensure that an autonomous vehicle travels within a road without exceeding road boundaries; the computer should plan the driving route of the autonomous vehicle correctly, and so on.

In conventional solutions, road boundaries are managed by discrete points. Thus, in determining the relative position of a target object, such as an autonomous vehicle, to a road boundary, the distance between the target object and each discrete point must first be calculated, and then the road boundary segment closest to the target object determined therefrom. Such a solution makes the determination of the nearest road boundary very inefficient.

Embodiments of the present disclosure propose a method for determining a nearest road boundary. In the embodiment of the disclosure, by utilizing the hierarchical data structure for representing the road boundary, the target segment closest to the target object can be efficiently determined from the multiple segments of the road boundary, and the efficiency of determining the closest road boundary is improved.

FIG. 1 illustrates an example environment 100 for determining a nearest road boundary in accordance with an embodiment of the present disclosure. As shown in FIG. 1, the example environment 100 includes a boundary determination device 110, a target object 120, and a road boundary 130. In some embodiments, the boundary-determination device 110 may be a computer installed on the target object 120. The target object 120 may be an autonomous vehicle. The road boundary 130 may be a road boundary in the real world. In some embodiments, the road boundary 130 may be characterized by pre-collected discrete points and stored in the boundary determination device 110. The example environment 100 also includes a hierarchical data structure 140 that characterizes road boundaries. In some embodiments, the hierarchical data structure 140 may be constructed in real-time by the boundary determination device 110 from pre-collected discrete points characterizing the roadway boundary 120. Alternatively, in other embodiments, the hierarchical data structure 140 may be pre-constructed and stored in the boundary-determination device 110. Various methods according to embodiments of the present disclosure may be implemented at the boundary determination device 110.

It should be understood that the example environment 100 for determining nearest road boundaries is described for exemplary purposes only and does not imply any limitation as to the scope of the present disclosure. For example, embodiments of the present disclosure may also be applied to environments other than the example environment 100. For example, the target object may be another vehicle or an obstacle on the road. It should also be understood that the specific numbers of devices or objects described above are given for illustrative purposes only and do not imply any limitation on the scope of the disclosure. For example, embodiments of the disclosure may also be applied to more or fewer devices or objects.

Fig. 2 shows a schematic diagram 200 of a method for determining a nearest road boundary in a conventional scheme. In conventional solutions, the road boundary is characterized by discrete points. For example, as shown in FIG. 2, the road boundary is characterized by A, B, C and D four points. Therefore, in determining the road boundary closest to the target object p, the distances between the target object p and each of the four points A, B, C and D are first calculated, and then the road boundary closest to the target object p is determined from the respective distances. In practice, the number of discrete points used to characterize a road boundary tends to be large, making it inefficient to determine the nearest road boundary with such a solution.

FIG. 3 shows a flowchart of an example method 300 for determining a nearest road boundary, in accordance with embodiments of the present disclosure. For example, the method 300 may be performed by the boundary determination device 110 as shown in fig. 1. It should be understood that method 300 may also be performed by other devices, and the scope of the present disclosure is not limited in this respect. It should also be understood that method 300 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect.

At 310, the boundary determination device 110 obtains location information of the target object 120. In some embodiments, the target object 120 may be an autonomous vehicle in which the boundary determination device 110 is located. Alternatively, in other embodiments, the target object 120 may be another vehicle in the vicinity of the boundary determination device 110 or an obstacle on the road, or the like. For example, in some embodiments, the boundary determination device 110 may obtain Global Positioning System (GPS) information of the target object 120.

At 320, the boundary determination device 110 obtains a hierarchical data structure 140 characterizing the road boundary 130, the hierarchical data structure 140 hierarchically storing location data of a plurality of segments of the road boundary 130.

In some embodiments, the hierarchical data structure 140 may be constructed in real-time by the boundary determination device 110 from pre-collected discrete points characterizing the roadway boundary 120. Alternatively, in other embodiments, the hierarchical data structure 140 may be pre-constructed and stored in the boundary-determination device 110.

In some embodiments, the hierarchical data structure 140 may be stored in the boundary determination device 110. Alternatively, in other embodiments, the hierarchical data structure 140 may be stored in other devices than the boundary determination device 110.

In some embodiments, the location data of the plurality of segments of the road boundary 130 stored in the hierarchical data structure 140 indicates locations of endpoints of the plurality of segments. Additionally, in some embodiments, the location data is stored in a leaf node of the hierarchical data structure 140, a parent node of the leaf node storing the boundary data, the segment defined by the location data in the leaf node being within the bounding box defined by the boundary data in the parent node. Additionally, in some embodiments, the hierarchical data structure 140 may be an RTREE data structure. The hierarchical data structure 140 is described below with reference to fig. 4A and 4B.

Fig. 4A shows a schematic diagram 401 of road boundary characterization according to an embodiment of the present disclosure. Fig. 4B shows a schematic diagram 402 of a hierarchical data structure characterizing a road boundary according to an embodiment of the disclosure. As shown in fig. 4A, the road boundary is divided into eight segments, represented by AB, BC, CD, DE, EF, FG, GH, and HK, respectively. In building the hierarchical data structure as shown in fig. 4B, each leaf node stores location data indicating the end points of each segment. For example, a first leaf node stores the location data for endpoints a and B, a second leaf node stores the location data for endpoints B and C, and so on.

In the embodiment shown in fig. 4A, every two adjacent segments are surrounded by a first bounding box (indicated by a dash-dot line). For example, segments AB and BC are enclosed by the first enclosure 405-1, segments CD and DE are enclosed by the first enclosure 410-1, and so on. Every second first bounding box is bounded by a second bounding box (indicated by the dashed line). For example, the first enclosure box 405-1 and the first enclosure box 410-1 are enclosed by the second enclosure box 425-1, and the first enclosure box 415-1 and the first enclosure box 420-1 are enclosed by the second enclosure box 430-1. The entire road boundary is surrounded by a third bounding box 435-1 (represented by a solid line). Additionally, in the RTREE data structure, such bounding boxes are also referred to as minimum bounding boxes (MBRs). In such an embodiment, in building the hierarchical data structure as shown in FIG. 4B, boundary data for bounding box 405-1, e.g., two diagonal vertices of bounding box 405-1, and pointers to leaf nodes AB and BC may be stored in parent node 405-2, boundary data for bounding box 425-1, e.g., two diagonal vertices of bounding box 425-1, and pointers to its child node 405-2 and child node 410-2, and so on, may be stored in parent node 425-2.

It should be understood that the hierarchical data structure 402 is shown for exemplary purposes only and does not imply any limitation as to the scope of the present disclosure. For example, each parent node in the hierarchical data structure may be associated with more or fewer child nodes; different parent nodes may be associated with different numbers of child nodes, and so on. The present disclosure is not limited in this respect.

At 330, the boundary determining apparatus 110 determines a target segment, which is a closest segment to the target object among the plurality of segments, from among the plurality of segments of the road boundary according to the position information of the target object and the hierarchical data structure.

Still referring to fig. 4A and 4B, if a target segment closest to the target object p as shown in fig. 4A is to be determined, the target segment closest to the target object p may be determined to be HK by querying, by the boundary determining device 110, the node 430-2, the node 420-2, and the node HK from the root node 435-2 in order using the hierarchical data structure. In this way, the query complexity can be reduced from O (n) to O (logn). Details on how to determine the target segment from the plurality of segments will be described below with reference to fig. 5.

In the above-described exemplary embodiments, by using the hierarchical data structure that characterizes the road boundary, it is possible to efficiently determine the target segment that is closest to the target object from among a plurality of segments of the road boundary without calculating the distances between the target object and all points that represent the road boundary, thereby improving the efficiency of determining the closest road boundary.

Additionally, in some embodiments, after determining a target segment closest to the target object from the plurality of segments of the road boundary, the boundary determination device 110 may also perform driving decisions and/or route planning as a function of the distance between the target object and the target segment. For example, in some embodiments, if the target object is a vehicle in which the boundary determination device 110 is located, the boundary determination device 110 may determine whether the vehicle is inside or outside the road based on the distance between the vehicle and the target segment. If it is outside the road, the boundary determining device 110 may control the vehicle to adjust the driving route so that it can be ensured that the vehicle is safely driven inside the road. Alternatively or additionally, in other embodiments, if the target object is another vehicle in the vicinity of the boundary determination device 110, the boundary determination device 110 may also make driving decisions and/or route planning based on the distance of the vehicle in which it is located from the road boundary, the distance of the other vehicle from the road boundary, and/or the distance between the vehicle in which it is located and the other vehicle, e.g., to decide whether it is safe to cut in and/or what route to take to cut in.

Fig. 5 illustrates a flow diagram of an example method 500 for determining a target segment closest to a target object in a road boundary, in accordance with an embodiment of the disclosure. For example, the method 500 may be performed by the boundary determination device 110 as shown in fig. 1. It should be understood that method 500 may also be performed by other devices, and the scope of the present disclosure is not limited in this respect. It should also be understood that method 500 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect.

At 510, the boundary determination device 110 may obtain boundary data in a plurality of parent nodes of the hierarchical data structure, starting from the root node. The multiple parent nodes are children of the root node of the hierarchical data structure, such as nodes 425-2 and 430-2 in FIG. 4B.

Referring to fig. 4A and 4B, if a target segment closest to the target object p as shown in fig. 4A is to be determined, the boundary determining device 110 may acquire boundary data in a plurality of parent nodes of the hierarchical data structure. In the hierarchical data structure shown in fig. 4B, since the hierarchical data structure has a plurality of layers of data nodes, the boundary determining device 110 may first acquire boundary data stored in the two child nodes 425-2 and 430-2 to which the root node 435-2 points. After determining that the target object p is closer to the node 430-2, boundary data stored in the two child nodes 415-2 and 420-2 to which the node 430-2 points is obtained. In the hierarchical data structure shown in FIG. 4B, node 415-2 is the parent of leaf nodes EF and FG, and node 420-2 is the parent of leaf nodes GH and HK. The process of determining a closer node from nodes 425-2 and 430-2 is similar to the process of determining the target parent node of a leaf node described below.

At 520, boundary determining device 110 may determine a target parent node, also referred to as a "first target parent node," from the plurality of parent nodes based on boundary data in the plurality of parent nodes. The bounding box corresponding to the first target parent node is a bounding box closest to the target object among the bounding boxes corresponding to the plurality of parent nodes.

As described above with respect to fig. 4A and 4B, the boundary data of the bounding box corresponding to the parent node is stored in the parent node, and therefore, after the boundary determining device 110 acquires the boundary data of each parent node, the bounding box defined by the boundary data may be determined, and then the distance between the target object and each bounding box may be determined, thereby determining the bounding box closest to the target object.

In embodiments of the present disclosure, various methods may be employed to calculate the distance between the target object and each bounding box. For example, in some embodiments, the distance between the target object and the center point of the bounding box may be calculated. Alternatively or additionally, in other embodiments, the distance between the target object and a certain edge of the bounding box may be calculated. Alternatively or additionally, in still other embodiments, it may be determined first whether the target object is within the spatial range defined by the bounding box, and so on. The present disclosure is not limited in this respect.

At 530, boundary determining device 110 may determine at least one target leaf node associated with the first target parent node. In some embodiments, if the child node of the first target parent node is a leaf node, the boundary determination device 110 may determine the child node of the first target parent node as at least one target leaf node.

In some embodiments, if the child nodes of the first target parent node are still parent nodes, the boundary determination device 110 may continue searching for parent nodes of the next level until the determined child nodes of the target parent node are leaf nodes. For example, the boundary determining device 110 may determine another target parent node, which is also referred to as a "second target parent node", from the child nodes of the first target parent node based on boundary data in the child nodes of the first target parent node. The bounding box corresponding to the second target parent node is the closest bounding box to the target object among the bounding boxes corresponding to the children of the first target parent node. If the child node of the second target parent node is a leaf node, the boundary determination device 110 may determine the child node of the second target parent node as at least one target leaf node. For example, in the hierarchical data structure shown in FIG. 4B, after determining that the node 430-2 that is closer to the target object is from among the nodes 425-2 and 430-2, the boundary determination device 110 may determine that the child nodes of the node 430-2 are the nodes 415-2 and 420-2, and then after determining that the node 420-2 that is closer to the target object is from among the nodes 415-2 and 420-2, the boundary determination device 110 may determine that the leaf nodes of the node 420-2 are the nodes GH and HK.

At 540, the boundary determination device 110 determines a target segment from the at least one segment corresponding to the at least one target leaf node based on the location data in the at least one target leaf node.

For example, in the hierarchical data structure shown in fig. 4B, after determining that the leaf nodes GH and HK are closer to the target object p, the boundary determining device 110 may determine the distance of the target object p from the segments defined by the end point data in the leaf nodes GH and HK, and then determine the target segment from the segments GH and HK as the segment HK according to the distance of the target object p from the segments GH and HK.

In the above-described exemplary embodiment, by determining the distance between the target object and the bounding box corresponding to the node hierarchically, the target segment closest to the target object can be determined hierarchically, thereby achieving efficient determination of the closest road boundary.

Examples of the method according to the present disclosure have been described in detail above with reference to fig. 1 to 5, in the following the implementation of the respective apparatus and device will be described.

According to an exemplary implementation of the present disclosure, an apparatus 600 for determining a nearest road boundary is provided. The apparatus 600 for determining a nearest road boundary includes a location information acquisition module 610, a hierarchical data structure acquisition module 620, and a first target segment determination module 630. The position information acquisition module 610 is configured to acquire position information of a target object; the hierarchical data structure acquisition module 620 is configured to acquire a hierarchical data structure representing a road boundary, the hierarchical data structure hierarchically storing location data of a plurality of segments of the road boundary; the first target segment determination module 630 is configured to determine a target segment from a plurality of segments of the road boundary, the target segment being a closest segment to the target object from the plurality of segments, based on the location information and the hierarchical data structure.

In some embodiments, the location data indicates locations of endpoints of the plurality of segments.

In some embodiments, the location data is stored in a leaf node of the hierarchical data structure, a parent node of the leaf node storing the boundary data, the segment defined by the location data in the leaf node being within a bounding box defined by the boundary data in the parent node.

In some embodiments, the first target segmentation determination module 630 comprises: a first boundary data acquisition module configured to acquire boundary data in a plurality of parent nodes in the hierarchical data structure, the plurality of parent nodes being child nodes of a root node of the hierarchical data structure, such as nodes 425-2 and 430-2 described with reference to fig. 4B; a first target parent node determination module configured to determine a first target parent node from a plurality of parent nodes based on boundary data in the plurality of parent nodes, such as node 430-2 described above with reference to fig. 4B, the bounding box corresponding to the first target parent node being the closest bounding box to the target object among the plurality of bounding boxes corresponding to the plurality of parent nodes; a first leaf node determination module configured to determine at least one target leaf node associated with a first target parent node, such as leaf nodes GH and HK described above with reference to fig. 4B; and a second target segment determination module configured to determine a target segment from at least one segment corresponding to the at least one target leaf node based on the location data in the at least one target leaf node, such as segment HK described above with reference to fig. 4B. .

In some embodiments, the first leaf node determination module may include: a second boundary data acquisition module configured to acquire boundary data in child nodes of the first target parent node; a second target parent node determination module configured to determine a second target parent node from the child nodes of the first target parent node based on the boundary data in the child nodes of the first target parent node, as described above with reference to node 420-2 of fig. 4B, the bounding box corresponding to the second target parent node being the closest bounding box to the target object among the bounding boxes corresponding to the child nodes of the first target parent node; a second leaf node determination module configured to: determining a child node of the second target parent node as at least one target leaf node if it is determined that the child node of the second target parent node is a leaf node;

in some embodiments, the second target segmentation determination module comprises: a distance determination module configured to determine a distance of the target object from at least one segment defined by the location data in the at least one target leaf node; and a third target segmentation determination module configured to determine a target segmentation from the at least one segmentation based on a distance of the target object from the at least one segmentation.

In some embodiments, the apparatus 600 further comprises: a decision and plan execution module configured to execute a driving decision and/or route planning based on a distance of the target object from the target segment.

Accordingly, some embodiments of the present disclosure propose an apparatus for determining a nearest road boundary, which is capable of efficiently determining a target segment closest to a target object from among a plurality of segments of a road boundary without calculating distances between the target object and all points used to represent the road boundary, by using a hierarchical data structure representing the road boundary, thereby improving efficiency in determining the nearest road boundary.

Fig. 7 illustrates a block diagram of a computing device/server 700 in which one or more embodiments of the present disclosure may be implemented. It should be understood that the computing device/server 700 illustrated in FIG. 7 is merely exemplary and should not be construed as limiting in any way the functionality and scope of the embodiments described herein.

As shown in fig. 7, computing device/server 700 is in the form of a general purpose computing device. Components of computing device/server 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. The processing unit 710 may be a real or virtual processor and may be capable of performing various processes according to programs stored in the memory 720. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capabilities of computing device/server 700.

Computing device/server 700 typically includes a number of computer storage media. Such media may be any available media that is accessible by computing device/server 700 and includes, but is not limited to, volatile and non-volatile media, removable and non-removable media. Memory 720 may be volatile memory (e.g., registers, cache, Random Access Memory (RAM)), non-volatile memory (e.g., Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory), or some combination thereof. Storage 730 may be a removable or non-removable medium and may include a machine-readable medium, such as a flash drive, a magnetic disk, or any other medium, which may be capable of being used to store information and/or data (e.g., training data for training) and which may be accessed within computing device/server 700.

Computing device/server 700 may further include additional removable/non-removable, volatile/nonvolatile storage media. Although not shown in FIG. 7, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 720 may include a computer program product 725 having one or more program modules configured to perform the various methods or acts of the various embodiments of the disclosure.

Communication unit 740 enables communication with other computing devices over a communication medium. Additionally, the functionality of the components of computing device/server 700 may be implemented in a single computing cluster or multiple computing machines capable of communicating over a communications connection. Thus, computing device/server 700 may operate in a networked environment using logical connections to one or more other servers, network Personal Computers (PCs), or another network node.

Input device 750 may be one or more input devices such as a mouse, keyboard, trackball, or the like. Output device 760 may be one or more output devices such as a display, speakers, printer, or the like. Computing device/server 700 may also communicate with one or more external devices (not shown), such as storage devices, display devices, etc., as desired through communication unit 740, with one or more devices that enable a user to interact with computing device/server 700, or with any device (e.g., network card, modem, etc.) that enables computing device/server 700 to communicate with one or more other computing devices. Such communication may be performed via input/output (I/O) interfaces (not shown).

According to an exemplary implementation of the present disclosure, a computer-readable storage medium having stored thereon computer-executable instructions is provided, wherein the computer-executable instructions are executed by a processor to implement the above-described method. According to an exemplary implementation of the present disclosure, there is also provided a computer program product, tangibly stored on a non-transitory computer-readable medium and comprising computer-executable instructions, which are executed by a processor to implement the method described above.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus, devices and computer program products implemented in accordance with the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable information presentation device to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable information presentation device, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable information presentation device, and/or other apparatus to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer-readable program instructions may be loaded onto a computer, other programmable information presentation apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various implementations of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing has described implementations of the present disclosure, and the above description is illustrative, not exhaustive, and not limited to the implementations disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terminology used herein was chosen in order to best explain the principles of various implementations, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand various implementations disclosed herein.