Road network topology construction method and device, vehicle and storage medium

Abstract

The application relates to a road network topology construction method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring a constructed road and acquiring a lane center line in the road, wherein the road comprises a preceding road and a subsequent road; acquiring a learning track; determining an exit lane center line of the learning track on the preceding road and determining an entrance lane center line of the learning track on the subsequent road; generating a topology connection line of the preceding road and the subsequent road according to the exit lane center line and the entrance lane center line and referring to the learning track; and obtaining a road network topology between the preceding road and the subsequent road according to the topology connection line. The scheme provided by the application can improve the construction quality of the road network topology and improve the safety and experience of automatic driving.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, and in particular to a road network topology construction method and device, a vehicle and a storage medium. BACKGROUND

[0002] Automatic driving is a technology that uses various sensors, computer vision, artificial intelligence and machine learning to perceive, analyze and make decisions about the road environment, so as to realize autonomous navigation and control of vehicles.

[0003] With the continuous development of automatic driving technology, AI (Artificial Intelligence) driving has emerged. AI driving generally refers to automatic driving of a vehicle in commuting mode. AI driving is based on strong autonomous learning ability and precise route memory when a user manually drives, and can record and generate a learning trajectory through one-time learning, and then execute automatic driving according to the learning trajectory. AI driving can not depend on high-precision maps and is not limited by ODD (Operational Design Domain).

[0004] Road network topology, i.e., the connectivity of roads in space, i.e., the topological relationship, is crucial for route planning and navigation in automatic driving. The construction quality of road network topology also affects the safety and experience of automatic driving. Therefore, how to improve the construction quality of road network topology is a problem to be solved in the field of automatic driving technology. SUMMARY

[0005] To solve or partially solve the problems in the related art, the present application provides a road network topology construction method and device, a vehicle and a storage medium, which can improve the construction quality of road network topology and improve the safety and experience of automatic driving.

[0006] The first aspect of the present application provides a road network topology construction method, comprising:

[0007] obtaining a constructed road and obtaining a lane center line in the road, wherein the road comprises a predecessor road and a successor road;

[0008] obtaining a learning trajectory;

[0009] determining an exit lane center line of the learning trajectory on the predecessor road and determining an entry lane center line of the learning trajectory on the successor road;

[0010] generating a topological connection line between the predecessor road and the successor road according to the exit lane center line and the entry lane center line and referring to the learning trajectory;

[0011] According to the topological connection line, a road network topology between the predecessor road and the successor road is obtained.

[0012] In an embodiment, the generating the topological connection line of the predecessor road and the successor road according to the exit lane centerline and the entry lane centerline and referring to the learning trajectory comprises:

[0013] A first topological relationship between the exit lane centerline and the entry lane centerline is constructed, and a first topological connection line of the predecessor road and the successor road is generated by referring to the learning trajectory;

[0014] A second topological relationship between the lane centerline of the predecessor road except the exit lane centerline and the lane centerline of the successor road except the entry lane centerline is constructed, and a second topological connection line of the predecessor road and the successor road is generated by referring to the learning trajectory;

[0015] The obtaining the road network topology between the predecessor road and the successor road according to the topological connection line comprises:

[0016] According to the first topological connection line and the second topological connection line, a road network topology between the predecessor road and the successor road is obtained.

[0017] In an embodiment, the predecessor road comprises a predecessor lane centerline, and the determining the learning trajectory at the exit lane centerline of the predecessor road comprises:

[0018] In the predecessor lane centerline, a predecessor lane centerline with a distance to an exit end face of the predecessor road less than a first threshold value is screened out;

[0019] A tail point of the screened-out predecessor lane centerline is projected to the learning trajectory as a reference line to obtain a first lateral distance value to the learning trajectory after the projection;

[0020] The predecessor lane centerline with the smallest first lateral distance value is taken as the learning trajectory at the exit lane centerline of the predecessor road.

[0021] In an embodiment, the successor road comprises a successor lane centerline, and the determining the learning trajectory at the entry lane centerline of the successor road comprises:

[0022] In the successor lane centerline, a successor lane centerline with a distance to an entry end face of the successor road less than a second threshold value is screened out;

[0023] A starting point of the screened-out successor lane centerline is projected to the learning trajectory as a reference line to obtain a second lateral distance value to the learning trajectory after the projection;

[0024] the successor road.

[0025] In an embodiment, the constructing the second topological relationship between the lane centerline of the predecessor road except the exit lane centerline and the lane centerline of the successor road except the entry lane centerline comprises:

[0026] projecting the lane centerline of the predecessor road except the exit lane centerline to the learning trajectory to obtain a third lateral distance value between the projected lane centerline and the learning trajectory;

[0027] projecting the lane centerline of the successor road except the entry lane centerline to the learning trajectory to obtain a fourth lateral distance value between the projected lane centerline and the learning trajectory;

[0028] constructing the second topological relationship between the two lane centerlines corresponding to the minimum deviation between the third lateral distance value and the fourth lateral distance value.

[0029] In an embodiment, the generating the topological connection line between the predecessor road and the successor road according to the exit lane centerline and the entry lane centerline and referring to the learning trajectory comprises:

[0030] generating a smooth trajectory as the topological connection line between the predecessor road and the successor road according to a preset smoothing algorithm and referring to the learning trajectory between the end point of the exit lane centerline and the start point of the entry lane centerline.

[0031] The second aspect of the present application provides a road network topology construction device, comprising:

[0032] a first acquisition module configured to acquire a constructed road and lane centerlines in the road, wherein the road comprises a predecessor road and a successor road;

[0033] a second acquisition module configured to acquire a learning trajectory;

[0034] a first processing module configured to determine the exit lane centerline of the learning trajectory in the predecessor road and the entry lane centerline of the learning trajectory in the successor road;

[0035] a second processing module configured to generate a topological connection line between the predecessor road and the successor road according to the exit lane centerline and the entry lane centerline and referring to the learning trajectory;

[0036] a result generation module configured to obtain a road network topology between the predecessor road and the successor road according to the topological connection line.

[0037] In an embodiment, the second processing module comprises:

[0038] a first topology processing submodule, configured to construct a first topology relationship between the exit lane centerline and the entry lane centerline, and generate a first topology connection line between the predecessor road and the successor road with reference to the learning trajectory;

[0039] a second topology processing submodule, configured to construct a second topology relationship between the lane centerline of the predecessor road except the exit lane centerline and the lane centerline of the successor road except the entry lane centerline, and generate a second topology connection line between the predecessor road and the successor road with reference to the learning trajectory;

[0040] The result generation module obtains a road network topology between the predecessor road and the successor road according to the first topology connection line and the second topology connection line.

[0041] In an embodiment, the first processing module comprises:

[0042] an exit lane centerline determination submodule, configured to screen, from the predecessor lane centerline, a predecessor lane centerline with a distance to an exit end face of the predecessor road less than a first threshold value, project an end point of the screened predecessor lane centerline to the learning trajectory as a reference line to obtain a first lateral distance value to the learning trajectory after projection, and take the predecessor lane centerline with the minimum first lateral distance value as the exit lane centerline of the predecessor road.

[0043] In an embodiment, the first processing module comprises:

[0044] an entry lane centerline determination submodule, configured to screen, from the successor lane centerline, a successor lane centerline with a distance to an entry end face of the successor road less than a second threshold value, project a start point of the screened successor lane centerline to the learning trajectory as a reference line to obtain a second lateral distance value to the learning trajectory after projection, and take the successor lane centerline with the minimum second lateral distance value as the entry lane centerline of the successor road.

[0045] The third aspect of the present application provides a vehicle, comprising:

[0046] a processor; and

[0047] a memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method described above.

[0048] The fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of a vehicle, causes the processor to perform the method described above.

[0049] The technical solution provided by the present application can include the following beneficial effects:

[0050] The technical solution of the present application acquires the constructed road and the lane center line in the road, and acquires the learning trajectory; then determines the learning trajectory on the exit lane center line of the predecessor road and determines the learning trajectory on the entrance lane center line of the successor road; then generates the topological connection line of the predecessor road and the successor road according to the exit lane center line and the entrance lane center line and referring to the learning trajectory; finally, acquires the road network topology between the predecessor road and the successor road according to the topological connection line. The present application constructs the road network topology by using the learning trajectory, learns from the prior information of the learning trajectory, and constructs the topology and generates the topological connection line between roads by determining the exit lane center line of the predecessor road and the entrance lane center line of the successor road, so as to improve the construction quality of the road network topology, improve the safety of autonomous driving and the experience of autonomous driving.

[0051] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0052] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the several views, and in which:

[0053] Figure 1 is a flowchart of a road network topology construction method shown in an embodiment of the present application;

[0054] Figure 2 is a flowchart of a road network topology construction method shown in another embodiment of the present application;

[0055] Figure 3 is a first schematic diagram of road network topology construction shown in an embodiment of the present application;

[0056] Figure 4 is a second schematic diagram of road network topology construction shown in an embodiment of the present application;

[0057] Figure 5 is a third schematic diagram of road network topology construction shown in an embodiment of the present application;

[0058] Figure 6is a fourth schematic diagram of road network topology construction shown by an embodiment of the present application;

[0059] Figure 7 is a fifth schematic diagram of road network topology construction shown by an embodiment of the present application;

[0060] Figure 8 is a sixth schematic diagram of road network topology construction shown by an embodiment of the present application;

[0061] Figure 9 is a seventh schematic diagram of road network topology construction shown by an embodiment of the present application;

[0062] Figure 10 is a structural schematic diagram of a road network topology construction device shown by an embodiment of the present application;

[0063] Figure 11 is a structural schematic diagram of a road network topology construction device shown by another embodiment of the present application;

[0064] Figure 12 is a structural schematic diagram of a vehicle shown by an embodiment of the present application. DETAILED DESCRIPTION

[0065] Embodiments of the present application will be described in more detail by referring to the drawings. Although embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided so that the present application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0066] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the description of the application and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0067] It should be understood that although the terms "first", "second", "third", etc. can be employed in this application to describe various information, such information should not be limited by these terms. These terms are only used to distinguish one piece of information from another. For example, a first information can also be termed a second information, and, similarly, a second information can also be termed a first information, without departing from the scope of the present application. Therefore, features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0068] Road network topology is crucial for route planning and navigation in autonomous driving. The construction quality of road network topology can affect the safety and experience of autonomous driving. How to improve the construction quality of road network topology is a problem to be solved in autonomous driving technology. The present application provides a road network topology construction method, which can improve the construction quality of road network topology and improve the safety and experience of autonomous driving.

[0069] The technical solutions of the embodiments of the present application are described in detail below with reference to the drawings.

[0070] Figure 1 is a flowchart of the road network topology construction method shown in the embodiments of the present application. The embodiments of the present application generate road network topology between roads with reference to learning trajectories on the basis of roads and lanes that have been constructed.

[0071] Referring to Figure 1 , the method comprises:

[0072] S101, acquiring a constructed road and acquiring a lane center line in the road, wherein the road comprises a predecessor road and a successor road.

[0073] The road can be constructed by a preset module, and can be constructed based on information detected by perception and using related technologies. The road construction method is not limited in the embodiments of the present application.

[0074] The road contains lanes, and the lane has a lane center line. Generally, a road containing several lanes corresponds to several road center lines.

[0075] Generally, two roads are needed to construct network topology, one as a predecessor and one as a successor. The first road appearing in front of the ego vehicle can be defined as the predecessor road, and the road following the predecessor road can be called the successor road. The predecessor road includes a predecessor lane center line, and the successor road includes a successor lane center line.

[0076] S102, acquiring a learning trajectory.

[0077] The technical solutions of the embodiments of the present application are to construct network topology between roads with reference to learning trajectories. The learning trajectory in the embodiments of the present application can be a driving trajectory of a vehicle in a commuting mode, i.e., an AI chauffeur.

[0078] The embodiments of the present application can acquire a learning trajectory of a user. The learning trajectory of the user can be acquired at the vehicle end or uploaded from the cloud. The learning trajectory can include recorded trajectory information of a vehicle driven by the ego vehicle, wherein the trajectory information includes coordinate information of a series of trajectory points sorted by time, and the trajectory points are all recorded trajectory points when learning a route.

[0079] S103, determine the exit lane center line of the predecessor road and the entrance lane center line of the successor road according to the learning trajectory.

[0080] Among them, the predecessor lane center line with a distance less than the first threshold value from the exit end face of the predecessor road can be screened out in the predecessor lane center line;

[0081] The end point of the screened out predecessor lane center line is projected to the learning trajectory to obtain a first lateral distance value from the learning trajectory;

[0082] The predecessor lane center line with the smallest first lateral distance value is taken as the exit lane center line of the predecessor road.

[0083] Among them, the successor lane center line with a distance less than the second threshold value from the entrance end face of the successor road can be screened out in the successor lane center line;

[0084] The starting point of the screened out successor lane center line is projected to the learning trajectory to obtain a second lateral distance value from the learning trajectory;

[0085] The successor lane center line with the smallest second lateral distance value is taken as the entrance lane center line of the successor road.

[0086] S104, according to the exit lane center line and the entrance lane center line, and referring to the learning trajectory, a topological connection line of the predecessor road and the successor road is generated.

[0087] Among them, the exit lane center line and the entrance lane center line can be constructed into a first topological relationship, and a first topological connection line of the predecessor road and the successor road is generated by referring to the learning trajectory;

[0088] The lane center line of the predecessor road except the exit lane center line and the lane center line of the successor road except the entrance lane center line are constructed into a second topological relationship, and a second topological connection line of the predecessor road and the successor road is generated by referring to the learning trajectory.

[0089] Among them, the lane center line of the predecessor road except the exit lane center line and the lane center line of the successor road except the entrance lane center line are constructed into a second topological relationship, including:

[0090] The lane center line of the predecessor road except the exit lane center line is projected to the learning trajectory to obtain a third lateral distance value from the learning trajectory;

[0091] The lane center line of the successor road except the entrance lane center line is projected to the learning trajectory to obtain a fourth lateral distance value from the learning trajectory;

[0092] The second topological relationship is constructed when the deviation between the third lateral distance value and the fourth lateral distance value is smallest.

[0093] The smooth trajectory is generated as the topological connection line between the predecessor road and the successor road from the end point of the exit lane center line to the start point of the entry lane center line according to a preset smoothing algorithm and with the learning trajectory as a reference.

[0094] S105, obtaining the road network topology between the predecessor road and the successor road according to the topological connection line.

[0095] The road network topology between the predecessor road and the successor road is obtained according to the first topological connection line and the second topological connection line.

[0096] The technical scheme of the embodiment of the application obtains the constructed road and the lane center line in the road, and obtains the learning trajectory. Then, the exit lane center line of the predecessor road and the entry lane center line of the successor road are determined. Then, the topological connection line between the predecessor road and the successor road is generated according to the exit lane center line and the entry lane center line and with the learning trajectory as a reference. Finally, the road network topology between the predecessor road and the successor road is obtained according to the topological connection line. The road network topology is constructed by using the learning trajectory, the prior information of the learning trajectory is used for reference, and the topology is constructed and the topological connection line between the roads is generated by determining the exit lane center line of the predecessor road and the entry lane center line of the successor road. Therefore, the construction quality of the road network topology can be improved, and the safety of autonomous driving and the experience of autonomous driving can be improved.

[0097] Figure 2 is a flowchart of a road network topology construction method according to another embodiment of the application.

[0098] The technical scheme of the embodiment of the application constructs the road network topology between the roads by using the learning trajectory on the basis of the road (road) and the lane (lane center line) that have been constructed based on the environmental perception information. The network topology between the roads is constructed based on the learning trajectory. According to the network topology and the topological connection line, a path that is inclined to be driven by a person can be generated for a user to use, and it can be ensured that the generated path is safe, passable, and conforms to the driving preference of a person.

[0099] Referring to Figure 2 The method comprises the following steps.

[0100] S201, obtaining a constructed road and a lane center line in the road, wherein the road comprises a predecessor road and a successor road.

[0101] Referring to Figure 3, Figure 3 The two rectangular boxes are roads that have been constructed, and the dashed lines in the rectangular boxes are lanes (center lines of the lanes) in the constructed roads. The road is the entire drivable area, and the road contains multiple lanes. The lane is the center line of the lanes, and therefore, a road containing several lanes corresponds to several lanes.

[0102] For the two roads in Figure 3 For the convenience of description, the two roads are defined as a parent road (a predecessor road, i.e., the first road in front of the vehicle in the figure) and a child road (a successor road, i.e., the second road in front of the vehicle in the figure) in the embodiments of the present application. That is, the first road in front of the vehicle is defined as the parent road, and the road after the parent road is called the child road, and the order is generally irreversible.

[0103] Generally, two roads are needed to construct a network topology, one as a predecessor and one as a successor. That is, if the blank area between the two roads is regarded as a virtual connecting road, the parent road can be understood as the predecessor road, and the child road can be understood as the successor road.

[0104] The parent and child distinction is mainly used to reflect the relationship of the topology network, and the construction of the general network is to find the corresponding relationship from all the lanes of the predecessor road to all the lanes of the successor road.

[0105] The road corresponding to the rectangular box can be constructed by a preset module, can be constructed based on the information detected by perception and by using related technologies, and the road construction method is not limited in the embodiments of the present application. For example, in related technologies, the cloud and the vehicle end can independently construct the road structure, the cloud can generate a cloud road structure relying on the advantage of over-the-horizon, and the vehicle end can generate a vehicle end road structure relying on the perception information of the vehicle end. Alternatively, the cloud and the vehicle end can cooperate to construct the road structure.

[0106] The lane in the parent road can be called a parent lane, and the lane in the child road can be called a child lane, that is, the predecessor road includes the predecessor lane center line, and the successor road includes the successor lane center line.

[0107] S202, acquiring a learning trajectory.

[0108] In order to ensure the connectivity of the road, a network topology of the road is generally constructed. In order to ensure that at least one passable path is provided according to the network topology, the technical solutions of the embodiments of the present application are to construct the network topology between roads by taking the learning trajectory as a reference.

[0109] The embodiments of the present application can obtain the learning trajectory of the vehicle of the user. The learning trajectory of the user can be obtained at the vehicle end or uploaded from the cloud end. The learning trajectory can include recorded trajectory information of the vehicle, and the trajectory information includes a series of coordinate information of trajectory points sorted according to time, and the trajectory points are all recorded trajectory points in the learning route.

[0110] The learning trajectory in the embodiments of the present application can be a driving trajectory in the commuting mode of the vehicle, that is, the AI driving mode. The commuting mode in the embodiments of the present application can be understood in a broad sense as a driving mode adopted by the user on the commuting route on weekdays, and a driving mode adopted by the user on a high-frequency driving route between certain common places on weekends or holidays, and is not limited to travel on weekdays. For example, the user frequently goes back and forth between the residence and a fixed entertainment place on weekends, and the driving trajectory with the residence and the entertainment place as the starting and ending points is also applicable to the commuting mode. By using the AI driving function, the learning trajectory can be recorded and generated through one-time learning, and a certain number of learning trajectories can be stored, for example, the user can store 5 or 10 learning trajectories at the vehicle end or the cloud end. The length of each learning trajectory can be a preset length, for example, the maximum length can be 100 kilometers, but is not limited thereto.

[0111] S203, determining the exit lane center line of the learning trajectory on the predecessor road and the entrance lane center line of the learning trajectory on the successor road.

[0112] The embodiments of the present application can find the exit lane of the learning trajectory on the predecessor road and the entrance lane of the learning trajectory on the successor road according to the physical relationship between each lane and the learning trajectory. Referring to FIG. 4, the red line 401 represents the user trajectory, that is, the learning trajectory. Figure 4

[0113] The learning trajectory can be one of the external inputs for constructing the network topology of the road, and the lane is an internal input for constructing the network topology. There are many lanes in the road, but in the embodiments of the present application, the lanes on the end face of the road are mainly concerned for the construction of the network topology.

[0114] ​The predecessor lane center line with a distance less than a first threshold value from an exit end surface of the predecessor road can be filtered out in the predecessor lane center line, and the successor lane center line with a distance less than a second threshold value from an entry end surface of the successor road can be filtered out in the successor lane center line. The first threshold value and the second threshold value can be set according to experience or actual needs.

[0115] For example, referring to Figure 5 The parent lane with a distance close to the exit end surface of the parent road can be filtered out according to the distance. Similarly, the child lane with a distance close to the entry end surface of the child road can be filtered out. The filtered parent lane and child lane are used as internal input for constructing the network topology.

[0116] The lane can be on the left or right of the learning trajectory, and can be far or close to the learning trajectory. In the embodiment of the present application, the exit lane and the entry lane of the learning trajectory are found first.

[0117] The end point of the filtered predecessor lane center line can be projected to the learning trajectory to obtain a first lateral distance value from the learning trajectory.

[0118] The predecessor lane center line with the smallest first lateral distance value is used as the exit lane center line of the learning trajectory on the predecessor road.

[0119] The start point of the filtered successor lane center line can be projected to the learning trajectory to obtain a second lateral distance value from the learning trajectory.

[0120] The successor lane center line with the smallest second lateral distance value is used as the entry lane center line of the learning trajectory on the successor road.

[0121] For example, the learning trajectory can be used as a reference line, and the last point of the filtered parent lane can be projected to the trajectory. Each parent lane will output a lateral distance value after projection. Similarly, the first point of the filtered child lane can be projected to the trajectory, and each child lane will also output a lateral distance value after projection.

[0122] Then, the lane with the smallest lateral distance value in the parent lane is selected as the exit lane of the learning trajectory, and the lane with the smallest lateral distance value in the child lane is selected as the entry lane of the learning trajectory.

[0123] As Figure 5The three yellow small arrows in the parent lane indicate the last point of the three parent lanes, and the three green small arrows in the child lane indicate the first point of the three child lanes. Figure 5 The marks l0, l1, l2 in the parent lane are the transverse distance values of the three child lanes after being projected onto the trajectory, Figure 5 The marks l0', l1', l2' in the parent lane are the transverse distance values of the three parent lanes after being projected onto the trajectory.

[0124] From the comparison results after Figure 5 The exit lane of the learning trajectory is the leftmost lane in the parent road with a transverse distance value of l0', and the entry lane of the learning trajectory is the left lane in the child road with a transverse distance value of l0.

[0125] S204, the exit lane center line and the entry lane center line are constructed into a first topological relationship, and a first topological connection line between the predecessor road and the successor road is generated by referring to the learning trajectory.

[0126] The exit lane center line and the entry lane center line can be constructed into a first topological relationship, and a first topological connection line between the predecessor road and the successor road is generated by referring to the learning trajectory.

[0127] After obtaining the exit lane of the learning trajectory and the entry lane of the learning trajectory, the exit lane and the entry lane are taken as a group of topologies, a first topological relationship is constructed, and a first topological connection line between the two lanes is constructed by referring to the shape of the learning trajectory, which can be seen from Figure 6 .

[0128] Figure 6 In the black arrow connection line 601 in the parent road, the black arrow connection line 601 is a topological connection line between the two roads, which is equivalent to a path, that is, a vehicle can drive from the leftmost lane of the parent road to the leftmost lane of the child road according to the path formed by the topological connection line, and the topological selection is the most reasonable in the current straight driving condition, and there is no need to perform invalid lane changing.

[0129] S205, the lane center lines of the predecessor road except the exit lane center line are constructed into a second topological relationship with the lane center lines of the successor road except the entry lane center line, and a second topological connection line between the predecessor road and the successor road is generated by referring to the learning trajectory.

[0130] The lane center line of the predecessor road except the exit lane center line and the lane center line of the successor road except the entrance lane center line can be constructed into a second topological relationship, and a second topological connection line of the predecessor road and the successor road is generated by referring to the learning trajectory.

[0131] For example, the lane center line of the predecessor road except the exit lane center line is projected to the learning trajectory to obtain a third lateral distance value after projection and the learning trajectory;

[0132] The lane center line of the successor road except the entrance lane center line is projected to the learning trajectory to obtain a fourth lateral distance value after projection and the learning trajectory;

[0133] The two lane center lines corresponding to the minimum deviation between the third lateral distance value and the fourth lateral distance value are constructed into a second topological relationship.

[0134] After the first topological connection line between the exit lane and the entrance lane is constructed by taking the exit lane and the entrance lane as a group of topologies and referring to the shape of the learning trajectory, for the remaining lanes, the lane with a distance substantially consistent with the learning trajectory in the physical position can be taken as the next group of topologies based on the learning trajectory.

[0135] At this time, the processing method can be consistent with the method of searching for the exit lane and the entrance lane. For example, each of the remaining lanes can also be projected to the learning trajectory to obtain a respective lateral distance value, and then the two lanes corresponding to the minimum deviation of the lateral distance value are found as the input of a group of topologies.

[0136] For example, in Figure 5 , it can be clearly seen that the difference between the lateral distance values l1 and l1' is significantly smaller than the difference between the lateral distance values l1 and l2', and smaller than the difference between the lateral distance values l1 and l0'. Therefore, the two lanes corresponding to the lateral distance values l1 and l1' are a new group of network topologies. Other similar, for example, the two lanes corresponding to the lateral distance values l2 and l2' are a new group of network topologies.

[0137] Further referring to Figure 7 , from the position relationship, it can be known that the two lanes with the same color of the arrows can constitute two groups of topologies, that is, the two lanes marked as 71 and 71' corresponding to the yellow arrows constitute a group of topologies, and the two lanes marked as 72 and 72' corresponding to the blue arrows constitute a group of topologies.

[0138] Among them, a set of topologies constructed by the two lanes marked 71 and 71' can construct a second topological connection line between the roads by referring to the shape of the learning trajectory; a set of topologies constructed by the two lanes marked 72 and 72' can also construct a second topological connection line between the roads by referring to the shape of the learning trajectory.

[0139] Among them, the embodiment of the present application can generate a smooth trajectory as a topological connection line between the preceding road and the succeeding road from the end point of the center line of the exit lane to the starting point of the center line of the entrance lane based on a preset smoothing algorithm and with the learning trajectory as a reference.

[0140] The preset smoothing algorithm may be, for example, a smoothing algorithm based on curve interpolation (polynomial curve algorithm, spline curve algorithm), an A-star algorithm based on sampling, or the like.

[0141] For example, the A-star algorithm is a commonly used heuristic search algorithm with good efficiency and accuracy. It is widely used in the field of path planning. The path information generated by the A-star algorithm can be used to round the inflection points to achieve path smoothing.

[0142] It should be noted that the above solution is based on the simplest straight-ahead scenario, where the number of parent lanes and child lanes is equal. In reality, more complex scenarios may occur. The technical solution in this embodiment of the application constructs a road network topology based on user trajectories, thereby providing an optimal solution in many cases.

[0143] For example, U-turn is a common working condition, but because there is no support for high-precision maps, it is difficult to give a suitable reference path based solely on perception information. However, by using the technical solution of the embodiment of the present application, referring to the user trajectory, that is, the learning trajectory, for processing and constructing the network topology between roads, it can provide great help in generating the path.

[0144] by Figure 8 For example, the blue dashed line 801 with an arrow is the topological connection line of the constructed network topology, and the red dashed line 802 is the user trajectory, i.e., the learned trajectory, which is the driving trajectory in the U-turn scenario. Utilizing the technical solution of the embodiment of the present application, the exit lane and entrance lane of the learned trajectory can be found. Then, referring to the shape of the learned trajectory, a topological connection line that is almost identical to the learned trajectory can be constructed. This topological connection line is equivalent to a generated reference path, so that even in the U-turn scenario, the vehicle can pass through the U-turn area very smoothly and comfortably.

[0145] Because the learning trajectory has been acquired, between the exit lane and the entry lane, embodiments of the present application can utilize the designed path smoothing algorithm, taking the learning trajectory as the reference, referring to the shape of the learning trajectory, from the last point of the parent lane to the first point of the child lane, to generate a point with a similar learning trajectory shape based on the result calculated from the learning trajectory as the final output topology connection line, which is the final output topology path.

[0146] It should be noted that, in order to display the effect in Figure 8 , a gap is set between the topology path (planned path) and the learning trajectory given in Figure 8 . However, in fact, the topology path (planned path) is basically consistent with the learning trajectory. Similarly, the way of constructing the road network topology with reference to the learning trajectory in the embodiments of the present application is also similar for the processing of other scenes, such as some main auxiliary road switching scenes and complex scenes such as left turn and right turn.

[0147] S206, obtaining the road network topology between the predecessor road and the successor road according to the first topology connection line and the second topology connection line.

[0148] Among them, according to the first topology connection line and the second topology connection line, the complete road network topology between the predecessor road and the successor road can be obtained.

[0149] The first topology relationship and the first topology connection line constructed according to the exit lane and the entry lane, and the second topology relationship and the second topology connection line constructed according to other lanes except the exit lane and the entry lane, can obtain the complete network topology structure between two roads. The information contained in the complete network topology structure between two roads generally includes information such as how many parent lanes can go out, how many child lanes can go in, and which child lane is connected to after each parent lane goes out.

[0150] The complete network topology structure between two roads can be seen in Figure 9 . Figure 9 In , the blue lines with arrows 91, 92 and 93 are the topology connection lines of the constructed network topology, and the red trajectory 901 is the user trajectory, i.e. the learning trajectory.

[0151] It should be noted that after obtaining the complete road network topology between the preceding road and the subsequent road, the topological connection line in the network topology can be used as a driving path between roads for reference selection during automatic driving, or a driving path can be regenerated according to the topological connection line for reference selection during automatic driving, so that the user is given multiple path selections while at least one learning trajectory is followed during automatic driving, thereby improving the flexibility and traffic capacity of automatic driving.

[0152] It can be found that the embodiment of the application constructs a road network topology based on a learning trajectory, and finally outputs a topological path, which can not only ensure the safety of automatic driving, but also meet the kinematic and dynamic constraints of the vehicle. Because the learning trajectory is a driving trajectory learned by the user and is uploaded as a learning trajectory after being approved by the user, the learning trajectory can be confirmed to be safe and usable from the user's perspective, and is consistent with the user's preferences. Therefore, the topological path processed according to the user's learning results, i.e., the learning trajectory, can also ensure that the vehicle can safely drive through, for example, the vehicle can also be guaranteed to pass through the turning area very smoothly and comfortably.

[0153] The embodiment of the application constructs a road network topology based on a learning trajectory, which can provide stable and reliable input for path planning and prediction, so that the vehicle can follow the learning trajectory completely while having other more path selections, thereby improving the flexibility and traffic capacity of automatic driving. The embodiment of the application provides multiple selections while at least one learning trajectory is followed, which not only ensures that there is a road to walk, but also increases the diversity of selection and improves the user's automatic driving experience.

[0154] Corresponding to the foregoing application function implementation method embodiment, the application also provides a road network topology construction device, a vehicle and corresponding embodiments.

[0155] Figure 10 FIG. 1 is a structural schematic diagram of a road network topology construction device according to an embodiment of the application.

[0156] Referring to Figure 10 The road network topology construction device 100 provided by the embodiment of the application comprises a first acquisition module 101, a second acquisition module 102, a first processing module 103, a second processing module 104, and a result generation module 105.

[0157] The first obtaining module 101 is configured to obtain a constructed road and a lane center line in the road, wherein the road comprises a preceding road and a succeeding road. The road can be constructed by a preset module, can be constructed based on information detected by perception and by using a related technology, and the road construction method is not limited in the embodiments of the present application. The road comprises a lane, and the lane has a lane center line. Generally, a road comprising several lanes corresponds to several road center lines. The first road appearing in front of the ego vehicle can be defined as the preceding road, and the road after the preceding road can be referred to as the succeeding road. The preceding road comprises a preceding lane center line, and the succeeding road comprises a succeeding lane center line.

[0158] The second obtaining module 102 is configured to obtain a learning trajectory. The technical solution of the embodiments of the present application takes the learning trajectory as a reference to construct a network topology between roads. The learning trajectory in the embodiments of the present application can be a driving trajectory of a vehicle commuting mode, that is, an AI driving mode.

[0159] The first processing module 103 is configured to determine an exit lane center line of the learning trajectory on the preceding road and an entrance lane center line of the learning trajectory on the succeeding road.

[0160] The second processing module 104 is configured to generate a topology connection line of the preceding road and the succeeding road according to the exit lane center line and the entrance lane center line and by referring to the learning trajectory. The second processing module 104 can construct a first topology relationship of the exit lane center line and the entrance lane center line, generate a first topology connection line of the preceding road and the succeeding road by referring to the learning trajectory, construct a second topology relationship of the lane center line of the preceding road except the exit lane center line and the lane center line of the succeeding road except the entrance lane center line, and generate a second topology connection line of the preceding road and the succeeding road by referring to the learning trajectory.

[0161] The result generating module 105 is configured to obtain a road network topology between the preceding road and the succeeding road according to the topology connection line. The result generating module 105 can obtain the road network topology between the preceding road and the succeeding road according to the first topology connection line and the second topology connection line.

[0162] The device provided in the application is used for obtaining a constructed road, obtaining a lane center line in the road, and obtaining a learning trajectory; then determining a learning trajectory on an exit lane center line of a preceding road and determining a learning trajectory on an entrance lane center line of a subsequent road; then generating a topological connection line between the preceding road and the subsequent road according to the exit lane center line and the entrance lane center line and referring to the learning trajectory; and finally obtaining a road network topology between the preceding road and the subsequent road according to the topological connection line. The application constructs the road network topology by using the learning trajectory, learns from prior information of the learning trajectory, and constructs the topology and generates the topological connection line between the roads by determining the exit lane center line of the preceding road and the entrance lane center line of the subsequent road, so as to improve the construction quality of the road network topology and improve the safety and experience of autonomous driving.

[0163] Figure 11 FIG. 1 is a structural schematic diagram of a road network topology construction device according to another embodiment of the application.

[0164] Referring to Figure 11 A road network topology construction device 100 includes a first obtaining module 101, a second obtaining module 102, a first processing module 103, a second processing module 104, and a result generating module 105.

[0165] The second processing module 104 can include a first topological processing submodule 1041 and a second topological processing submodule 1042.

[0166] The first topological processing submodule 1041 is configured to construct a first topological relationship between the exit lane center line and the entrance lane center line, and generate a first topological connection line between the preceding road and the subsequent road by referring to the learning trajectory.

[0167] The second topological processing submodule 1042 is configured to construct a second topological relationship between lane center lines of the preceding road except the exit lane center line and lane center lines of the subsequent road except the entrance lane center line, and generate a second topological connection line between the preceding road and the subsequent road by referring to the learning trajectory.

[0168] The result generating module 105 obtains a road network topology between the preceding road and the subsequent road according to the first topological connection line and the second topological connection line.

[0169] The first processing module 103 can include an exit lane center line determining submodule 1031.

[0170] The exit lane centerline determination submodule 1031 is used to screen the centerlines of the preceding lane whose distance from the exit end face of the preceding road is less than a first threshold; using the learning trajectory as a baseline, project the end point of the screened centerline of the preceding lane onto the learning trajectory to obtain a first lateral distance value from the projected learning trajectory; and determine the centerline of the preceding lane with the smallest first lateral distance value as the exit lane centerline of the learning trajectory on the preceding road.

[0171] The first processing module 103 may include: an entrance lane centerline determination submodule 1032 .

[0172] The entry lane centerline determination submodule 1032 is configured to screen, from among the subsequent lane centerlines, those whose distance from the entry end face of the subsequent road is less than a second threshold. Using the learning trajectory as a baseline, the starting point of the screened successor lane centerline is projected onto the learning trajectory to obtain a second lateral distance from the projected learning trajectory. The successor lane centerline with the smallest second lateral distance is used as the entry lane centerline of the learning trajectory on the subsequent road.

[0173] The device shown in the embodiments of this application can construct a road network topology based on the learned trajectory, providing stable and reliable input for path planning and prediction. This allows the vehicle to fully follow the learned trajectory while also having more options for other routes, thereby improving the flexibility and traffic capacity of autonomous driving. Because the learned trajectory is a driving trajectory that the user has learned and uploaded as a learned trajectory after the user approves it, from the user's perspective, this learned trajectory can be confirmed to be safe, usable, and in line with the user's preferences.

[0174] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0175] Figure 12 It is a schematic structural diagram of a vehicle shown in an embodiment of the present application.

[0176] See also Figure 12 , vehicle 1000 includes a memory 1010 and a processor 1020 .

[0177] The processor 1020 can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor.

[0178] The memory 1010 can include various types of storage units, such as a system memory, a read-only memory (ROM), and a permanent storage device. Among them, the ROM can store static data or instructions required by the processor 1020 or other modules of the computer. The permanent storage device can be a readable and writable storage device. The permanent storage device can be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a mass storage device (such as a magnetic or optical disk, a flash memory) as a permanent storage device. In some other embodiments, the permanent storage device can be a removable storage device (such as a floppy disk, an optical drive). The system memory can be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory can store some or all instructions and data required by the processor during runtime. In addition, the memory 1010 can include a combination of any computer readable storage media, including various types of semiconductor memory chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic disks and / or optical disks. In some embodiments, the memory 1010 can include a readable and / or writable removable storage device, such as a compact disc (CD), a read-only digital versatile disc (such as DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a min SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. The computer readable storage medium does not include a carrier wave and a transient electronic signal transmitted through wireless or wired transmission.

[0179] The memory 1010 stores executable code, which, when processed by the processor 1020, can cause the processor 1020 to perform part or all of the above-mentioned methods.

[0180] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0181] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by the processor of a vehicle (or electronic device, or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0182] The embodiments of the present application have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method of constructing a road network topology, characterized by, The method comprises the following steps: acquiring a constructed road and acquiring a lane center line in the road, wherein the road comprises a preceding road and a subsequent road, the preceding road is a first road appearing in front of a vehicle, the subsequent road is a road behind the preceding road, the preceding road comprises a preceding lane center line, and the subsequent road comprises a subsequent lane center line; acquiring a learning track; determining an exit lane center line of the learning track on the preceding road and determining an entry lane center line of the learning track on the subsequent road; generating a topological connection line of the preceding road and the subsequent road according to the exit lane center line of the learning track on the preceding road and the entry lane center line of the learning track on the subsequent road and referring to the learning track, wherein the generating comprises: constructing a first topological relationship between the exit lane center line and the entry lane center line and generating a first topological connection line of the preceding road and the subsequent road by referring to the learning track; and constructing a second topological relationship between lane center lines of the preceding road except the exit lane center line and lane center lines of the subsequent road except the entry lane center line and generating a second topological connection line of the preceding road and the subsequent road by referring to the learning track; obtaining a road network topology between the preceding road and the subsequent road according to the topological connection line, wherein the obtaining comprises: obtaining the road network topology between the preceding road and the subsequent road according to the first topological connection line and the second topological connection line.

2. The method of claim 1, wherein, The preceding road comprises a preceding lane center line, and the determining of the exit lane center line of the learning track on the preceding road comprises: screening, in the preceding lane center line, a preceding lane center line with a distance to an exit end face of the preceding road less than a first threshold value; projecting, with the learning track as a reference line, an end point of the screened preceding lane center line onto the learning track to obtain a first lateral distance value of the learning track after the projection; taking, as the exit lane center line of the learning track on the preceding road, a preceding lane center line with the smallest first lateral distance value.

3. The method of claim 1, wherein, The subsequent road comprises a subsequent lane center line, and the determining of the entry lane center line of the learning track on the subsequent road comprises: screening, in the subsequent lane center line, a subsequent lane center line with a distance to an entry end face of the subsequent road less than a second threshold value; projecting, with the learning track as a reference line, a start point of the screened subsequent lane center line onto the learning track to obtain a second lateral distance value of the learning track after the projection; taking, as the entry lane center line of the learning track on the subsequent road, a subsequent lane center line with the smallest second lateral distance value.

4. The method of claim 1, wherein, The constructing of the second topological relationship between the lane center lines of the preceding road except the exit lane center line and the lane center lines of the subsequent road except the entry lane center line comprises: projecting, with the learning track as a reference line, the lane center lines of the preceding road except the exit lane center line onto the learning track to obtain third lateral distance values of the learning track after the projection; and projecting the lane center line of the successor road except the exit lane center line of the successor road to the learning trajectory to obtain a fourth lateral distance value of the learning trajectory after the projection; constructing a second topological relationship between the two lane center lines corresponding to the minimum deviation between the third lateral distance value and the fourth lateral distance value.

5. The method according to any one of claims 1 to 4, characterized in that, The generating of the topological connection line between the predecessor road and the successor road according to the exit lane center line and the entry lane center line and referring to the learning trajectory comprises: generating a smooth trajectory as the topological connection line between the predecessor road and the successor road from the end point of the exit lane center line to the start point of the entry lane center line according to a preset smoothing algorithm and referring to the learning trajectory.

6. A road network topology construction apparatus characterized by comprising: The method comprises: a first obtaining module, configured to obtain a constructed road and lane center lines in the road, wherein the road comprises a predecessor road and a successor road, the predecessor road is a first road appearing in front of a vehicle, the successor road is a road behind the predecessor road, the predecessor road comprises a predecessor lane center line, and the successor road comprises a successor lane center line; a second obtaining module, configured to obtain a learning trajectory; a first processing module, configured to determine the exit lane center line of the predecessor road of the learning trajectory and the entry lane center line of the successor road of the learning trajectory; a second processing module, configured to generate a topological connection line between the predecessor road and the successor road according to the exit lane center line of the predecessor road and the entry lane center line of the successor road of the learning trajectory and referring to the learning trajectory; a result generating module, configured to obtain a road network topology between the predecessor road and the successor road according to the topological connection line; wherein the second processing module comprises: a first topological processing submodule, configured to construct a first topological relationship between the exit lane center line and the entry lane center line, and generate a first topological connection line between the predecessor road and the successor road by referring to the learning trajectory; a second topological processing submodule, configured to construct a second topological relationship between the lane center line of the predecessor road except the exit lane center line and the lane center line of the successor road except the entry lane center line, and generate a second topological connection line between the predecessor road and the successor road by referring to the learning trajectory; the result generating module obtains the road network topology between the predecessor road and the successor road according to the first topological connection line and the second topological connection line.

7. The apparatus of claim 6, wherein, The first processing module comprises: an exit lane center line determining submodule, configured to filter, in the predecessor lane center line, a predecessor lane center line with a distance to an exit end face of the predecessor road less than a first threshold value, project an end point of the filtered predecessor lane center line to the learning trajectory to obtain a first lateral distance value of the learning trajectory after the projection, and take the predecessor lane center line with the minimum first lateral distance value as the exit lane center line of the learning trajectory in the predecessor road.

8. The apparatus of claim 6, wherein, The first processing module comprises: The entrance lane center line determination sub-module is configured to: in the subsequent lane center line, filter a subsequent lane center line with a distance to an entrance end face of a subsequent road less than a second threshold value; project a starting point of the filtered subsequent lane center line to a learning trajectory as a reference line to obtain a second lateral distance value of the subsequent lane center line to the learning trajectory after projection; and take the subsequent lane center line with the minimum second lateral distance value as the learning trajectory in the entrance lane center line of the subsequent road.

9. A vehicle characterized by comprising: Comprise: a processor; and a memory having stored thereon executable code that, when executed by the processor, causes the processor to perform the method of any one of claims 1-5.

10. A computer-readable storage medium, characterized in that: a memory having stored thereon executable code that, when executed by a processor of a vehicle, causes the processor to perform the method of any one of claims 1-5.

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