CN114103995A - Unmanned vehicle control method and device used in traffic intersection scene and unmanned vehicle - Google Patents

Unmanned vehicle control method and device used in traffic intersection scene and unmanned vehicle Download PDF

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Publication number
CN114103995A
CN114103995A CN202111403857.4A CN202111403857A CN114103995A CN 114103995 A CN114103995 A CN 114103995A CN 202111403857 A CN202111403857 A CN 202111403857A CN 114103995 A CN114103995 A CN 114103995A
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unmanned vehicle
intersection
road
traffic intersection
roads
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邹李兵
张海强
李成军
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Zhidao Network Technology Beijing Co Ltd
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Zhidao Network Technology Beijing Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)

Abstract

The application relates to an unmanned vehicle control method and device and an unmanned vehicle used in a crossing scene. The method comprises the following steps: planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map, and pre-loading the high-definition map of the appointed traffic intersection; when the unmanned vehicle is positioned in an intersection connection area of a traffic intersection in the path, obtaining vector information corresponding to a plurality of roads associated with the intersection connection area, determining a spatial position relation among the plurality of roads according to the vector information, and determining the road through which the path passes; and generating a behavior decision of the unmanned vehicle passing through the traffic intersection, and executing automatic driving according to the behavior decision. The scheme provided by the application avoids the defect that the scheme in the related technology occupies a large amount of computing resources, and is convenient to implement.

Description

Unmanned vehicle control method and device used in traffic intersection scene and unmanned vehicle
Technical Field
The application relates to the technical field of automatic driving, in particular to an unmanned vehicle control method and device and an unmanned vehicle used in a crossing scene.
Background
The automatic driving system utilizes various vehicle-mounted sensors and decision planning control algorithms to realize the behaviors of lane changing, confluence, overtaking, car following, steering and the like of the vehicle. Due to the diversity of the real road scenes, different behavior strategies need to be given for different scenes in the decision planning control.
In the related art, planning and controlling of unmanned vehicle behaviors in traffic intersection scenes are still a big difficulty in the field of automatic vehicle driving control, mainly due to complexity of the traffic intersection scenes. The traffic intersection scene is mainly aimed at deciding what action behaviors, such as straight running, left turning, right turning, turning around and the like, the unmanned vehicle should adopt at the intersection, and the behavior decision scheme for the traffic intersection scene in the related art is complex, occupies a large amount of computing resources and is difficult to implement.
Disclosure of Invention
In order to solve or partially solve the problems in the related art, the application provides the unmanned vehicle control method and device and the unmanned vehicle used in the traffic intersection scene, so that the defect that the scheme in the related art occupies a large amount of computing resources is overcome, and the implementation is convenient.
The application provides a first aspect of a method for controlling an unmanned vehicle in a traffic intersection scene, which comprises the following steps:
planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map, and pre-loading the high-definition map of the appointed traffic intersection;
when the unmanned vehicle is positioned in an intersection connecting area of the traffic intersection, obtaining vector information corresponding to a plurality of roads associated with the intersection connecting area, determining a spatial position relation among the plurality of roads according to the vector information, and determining the road through which the path passes;
and generating a behavior decision of the unmanned vehicle passing through the traffic intersection, and executing automatic driving according to the behavior decision.
In one implementation, before obtaining vector information corresponding to a plurality of roads associated with the intersection connecting area, the method includes:
and sequentially taking at least two position points on the path, searching whether the at least two position points are in the intersection connecting area or not in the high-definition map, and if so, obtaining the spatial position information of the road section where the current unmanned vehicle is located according to the vector information corresponding to the at least two position points. In one implementation manner, the obtaining vector information corresponding to a plurality of roads associated with the intersection connecting area includes:
obtaining a set of a plurality of roads associated with the intersection connecting area, sequentially searching the central line of each road in the set, sequentially determining at least two position points on the central line of each road, and obtaining vector information corresponding to each road.
In one implementation, the determining a spatial position relationship between a plurality of roads according to the vector information includes:
and determining included angles among a plurality of roads associated with the intersection connecting area according to the vector information.
In one implementation, after determining an included angle between a plurality of roads associated with the intersection connecting area according to the vector information, the method includes:
and sequencing the roads according to the included angles to generate a matrix.
In one implementation, the determining the road through which the path passes includes:
obtaining the spatial position information of the driving-in road section of the unmanned vehicle at the traffic intersection and obtaining the spatial position information of the driving-out road section of the unmanned vehicle at the traffic intersection;
and determining the road passed by the path according to the spatial position information of the driving-in road section and the driving-out road section.
In one implementation, the generating a behavior decision of the unmanned vehicle passing through the traffic intersection according to the spatial position relationship includes:
and respectively obtaining the positions and included angles of the driving-in road sections and the driving-out road sections in the matrix, and generating a behavior decision of the unmanned vehicle passing through the traffic intersection according to the positions and the included angles.
A second aspect of the present application provides an unmanned vehicle control apparatus for a traffic intersection scene, comprising:
the planning module is used for planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map;
the determining module is used for determining the spatial position relation among a plurality of roads associated with the intersection connecting area and determining the road passed by the path when the unmanned vehicle is positioned in the intersection connecting area of the traffic intersection in the path;
and the execution module is used for generating a behavior decision of the unmanned vehicle passing through the traffic intersection and executing automatic driving according to the behavior decision.
In one implementation, the determining module includes:
the position point acquisition sub-module is used for sequentially acquiring at least two position points on the path and searching whether the at least two position points are positioned in the intersection connecting area or not in the high-definition map;
and the processing submodule is used for obtaining the spatial position information of the road section where the current unmanned vehicle is located according to the vector information corresponding to the at least two position points if the position point obtaining submodule searches that the at least two position points are located in the intersection connecting area.
A third aspect of the present application provides an unmanned vehicle comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method as described above.
The technical scheme provided by the application can comprise the following beneficial effects:
according to the technical scheme provided by the embodiment of the application, firstly, a path from a starting point to a target point of an unmanned vehicle is planned, and a high-definition map of a specified traffic intersection is loaded in advance; and then when the unmanned vehicle is positioned in an intersection connecting area of the traffic intersection, obtaining vector information corresponding to a plurality of roads associated with the intersection connecting area, determining a spatial position relation among the plurality of roads according to the vector information, determining a road through which a path passes, finally generating a behavior decision of the unmanned vehicle passing through the traffic intersection, and executing automatic driving according to the behavior decision. After the processing, when the unmanned vehicle passes through the traffic intersection, the defect that the scheme in the related technology occupies a large amount of computing resources is avoided, and the implementation is convenient.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 is a schematic flow chart of an unmanned vehicle control method for a traffic intersection scene according to an embodiment of the present application;
FIG. 2 is another schematic flow chart diagram of an unmanned vehicle control method for use in a traffic intersection scene according to an embodiment of the present application;
FIG. 3 is another schematic flow chart diagram illustrating an unmanned vehicle control method for use in a traffic intersection scenario according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an unmanned vehicle control device for a traffic intersection scene according to an embodiment of the present application;
fig. 5 is another schematic structural diagram of an unmanned vehicle control device for a traffic intersection scene according to an embodiment of the present application;
fig. 6 is a schematic structural view of an unmanned vehicle according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While embodiments of the present application are illustrated in the accompanying drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments 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 terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this 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 should 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.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In the related art, planning and controlling of unmanned vehicle behaviors in traffic intersection scenes are still a big difficulty in the field of automatic unmanned vehicle driving control, mainly due to complexity of the traffic intersection scenes. The traffic intersection scene is mainly aimed at deciding what action behaviors, such as straight running, left turning, right turning, turning around and the like, the unmanned vehicle should adopt at the intersection, and the behavior decision scheme for the traffic intersection scene in the related art is complex, occupies a large amount of computing resources and is difficult to implement.
In view of the above problems, embodiments of the present application provide an unmanned vehicle control method and apparatus for use in a traffic intersection scene, and an unmanned vehicle, which simplify a method for processing an unmanned vehicle passing through a traffic intersection, avoid a defect that a scheme in the related art occupies a large amount of computing resources, and are convenient to implement.
The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an unmanned vehicle control method for a traffic intersection scene according to an embodiment of the present application.
Referring to fig. 1, a method of an embodiment of the present application includes:
step S101, planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map, and pre-loading the high-definition map of the appointed traffic intersection.
In this step, the unmanned vehicle is also called an unmanned vehicle or an autonomous vehicle, and the path planning may be based on an autonomous high-definition map, that is, a path connected between the start point and the target point may be determined on the high-definition map.
Step S102, when the unmanned vehicle is located in an intersection connection area of a traffic intersection in the path, obtaining vector information corresponding to a plurality of roads associated with the intersection connection area, determining a spatial position relation among the plurality of roads according to the vector information, and determining the road through which the path passes.
In the step, the intersection connecting area can be an area where a plurality of roads in a traffic intersection are connected, whether the current unmanned vehicle is located in the intersection connecting area of the traffic intersection can be searched in a data container of a high-definition map, when the unmanned vehicle is located in the intersection connecting area, vector information corresponding to the plurality of roads associated with the intersection connecting area is respectively obtained, and the spatial position relation among the plurality of roads is determined according to the obtained vector information.
The spatial position relationship may include, for example, positions of a plurality of roads, directions of the plurality of roads, and angles between the plurality of roads.
And S103, generating a behavior decision of the unmanned vehicle passing through the traffic intersection, and executing automatic driving according to the behavior decision.
In this step, a behavior decision for the autonomous vehicle to perform straight running, left turning, right turning, or turning around may be generated.
It can be seen that, the method provided by the embodiment of the application firstly plans a path from a starting point to a target point of the unmanned vehicle; and then when the unmanned vehicle is positioned in an intersection connecting area of the traffic intersection, acquiring the spatial position relation among a plurality of roads associated with the intersection connecting area through vector information, determining the road through which the path passes, finally generating a behavior decision of the unmanned vehicle passing through the traffic intersection, and executing automatic driving according to the behavior decision. After the treatment, the method for treating the traffic intersection by the unmanned vehicle is simplified, and the implementation is convenient.
Fig. 2 is another schematic flow chart of the unmanned vehicle control method for a traffic intersection scene according to the embodiment of the present application, and fig. 2 further describes the scheme of the embodiment of the present application compared with fig. 1.
Referring to fig. 2, the scheme of the embodiment of the present application includes:
step S201, planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map, and pre-loading the high-definition map of the appointed traffic intersection.
The path may be generated by a global path planning algorithm and stored discretely in the container V.
The global path planning algorithm of the present embodiment may include, for example, Dijkstra (Dijkstra algorithm) algorithm or a (a-Star) algorithm.
Dijkstra (Dijkstra) algorithm is a typical single-source shortest path algorithm and is used for calculating the shortest path from one node to all other nodes, and the method is mainly characterized in that the shortest path is expanded outwards layer by taking a starting point as a center until the shortest path is expanded to an end point. The A-Star algorithm is the most effective direct search method for solving the shortest path in the static road network, and is also an effective algorithm for solving a plurality of search problems, and the closer the distance estimation value in the algorithm is to the actual value, the faster the final search speed is.
Step S202, when the unmanned vehicle is in the intersection connection area of the traffic intersection, vector information corresponding to a plurality of roads associated with the intersection connection area is obtained.
In this step, at least two location points on the route may be sequentially taken, and whether the at least two location points of the location points are located in the traffic intersection connection area or not may be searched in the data container V.
At least two position points can be taken from the central line of the roads in sequence, and vector information corresponding to each road is obtained according to the at least two position points.
Step S203, determining included angles among a plurality of roads associated with the intersection connecting area according to the vector information, and sequencing the plurality of roads according to the included angles to generate a matrix.
In this step, the included angle between the vectors corresponding to each road can be calculated according to a preset formula, and since the vectors are on the central line of the road, the included angle between different vectors is also the included angle between different roads. Then, a plurality of roads are arranged counterclockwise according to the included angle from small to large to generate a matrix.
And step S204, generating a behavior decision of the unmanned vehicle passing through the traffic intersection according to the corresponding spatial position information of the driving-in road section and the driving-out road section of the unmanned vehicle at the traffic intersection in the matrix, and executing automatic driving.
In the step, position points corresponding to the driving-in road section and the driving-out road section can be sequentially taken from the path, vectors of the driving-in road section and the driving-out road section are obtained according to the position points, a behavior decision that the unmanned vehicle passes through the traffic intersection is generated according to the positions and included angles of the driving-in road section and the driving-out road section in the matrix, and the unmanned vehicle executes automatic driving according to the behavior decision.
It can be seen that, in the scheme provided by the embodiment of the application, the spatial position information of a plurality of roads is calculated by calculating the vectors of the plurality of roads associated with the intersection connection area, then the spatial position information corresponding to the driving-in road section and the driving-out road section of the unmanned vehicle in the matrix, such as the position and the included angle, is determined, and finally the behavior decision of the unmanned vehicle passing through the traffic intersection is generated.
Fig. 3 is another schematic flow chart of the unmanned vehicle control method for a traffic intersection scene according to the embodiment of the present application, and fig. 3 describes a scheme of the embodiment of the present application in more detail than fig. 1 and fig. 2.
Referring to fig. 3, the scheme of the embodiment of the present application includes:
step S301, a path from a starting point to a target point of the unmanned vehicle is planned, and a high-definition map of a specified traffic intersection is loaded in advance.
In this step, a path may be generated by a global path planning algorithm, and the path may be discretely stored in the container V, that is, high definition map data for automatic driving and planned path data may be stored in the data container V.
Step S302, at least two position points on the path are sequentially taken.
In this step, points P on the path can be taken in sequence in the data container ViWherein i represents the number of points to be taken of the position point, and if i is more than or equal to 2, the position point P can be takeni-1And the position point Pi-2，
Step S303, judging whether at least two position points are in an intersection connecting area, and if so, entering step S304; if not, the process returns to step S302 until the data container V is empty.
In this step, the position point P can be judgedi-2And the position point Pi-1Whether or not in the intersection connecting zone.
And step S304, acquiring the spatial position information of the driving road section of the current unmanned vehicle in the intersection connecting area.
In this step, if the position point PiWhen i is not less than 2, passing through the position point Pi-2，Pi-1Vector of composition (R)0,Pi-2，R0,Pi-1) To indicate the driving-in road section R of the current road at the traffic intersectioninThe driving road section is also the road section where the current unmanned vehicle is located in the intersection connecting area. Similarly, the outgoing road section R of the current road at the traffic intersection can be acquiredoutSpatial position information of (a).
Step S305, acquiring a road set of a plurality of roads associated with the intersection connecting area, and obtaining vector information corresponding to each road in the road set.
In this step, when the unmanned vehicle is in the intersection connection area, first, the set Route ═ R of all roads associated with the intersection connection area is searched0,R1,R2…RnR represents a road, n represents the number of roads; then, the user can use the device to perform the operation,sequentially searching the central line of each road R and establishing a rectangular coordinate system at the traffic intersection, and taking a first coordinate point (R) connected with the intersection connecting area on the central line of each corresponding road in the rectangular coordinate systemn,,P0) And a second subsequent coordinate point (R) on the centre line of each roadn,P1) Thus, vector coordinates (R) corresponding to each link can be composedn,P0，Rn,P1)。
And S306, determining included angles among a plurality of roads associated with the intersection connecting area according to the vector information, and sequencing the plurality of roads according to the included angles to generate a matrix.
In this step, the included angle between the multiple roads may be an included angle between adjacent roads, or an included angle between two different non-adjacent roads. In one implementation, the coordinates of the vector corresponding to each road may be calculated according to the coordinates of the acquired position point, and the included angle between the roads may be calculated according to the vector coordinates corresponding to each road, for example, when the included angle between different roads is R, the value of R may be calculated according to the following formula:
Figure BDA0003371643930000081
where a and b represent the coordinates of two vectors in space, vector a and vector b correspond to different roads, respectively, | a | and | b | are the moduli of vector a and vector b.
Then, the matrix MR may be generated by arranging the plurality of roads from small to large according to the included angles, the matrix MR may be generated by arranging the plurality of roads counterclockwise according to the size of the included angles with the center point as the center point, and as can be understood, the matrix may also be understood as a set in which the plurality of roads are arranged according to the size of the included angles.
And step S307, obtaining the space position information of the exiting road section when the current unmanned vehicle passes through the intersection connection area.
In this step, the position points P on the path may be sequentially takeniSearching for a location point P in high definition map dataiIf the intersection connection region is not passed, if the ID of the road remains unchanged, that is, the ID of the road in the high-precision map corresponds to the road information in the matrix MR, the ID of the road may be the road information recorded in the high-precision map data, and the road information in the matrix MR may be the road information calculated from the vectors of the road, such as the direction of the road, the included angle between the roads, and the like. Therefore, the current road section where the unmanned vehicle is located can be judged to be the outgoing road section after passing through the intersection connecting area.
And step S308, determining the positions and the included angles of the driving-in road section and the driving-out road section in the matrix, and determining the road passed by the route according to the positions and the included angles of the driving-in road section and the driving-out road section.
For example, since the spatial position relationship of a plurality of roads is included in the matrix MR, the entry route section R can be searched for in the matrix MRinAnd driving out the road section RoutThe corresponding position T and the Angle.
And step S309, generating a behavior decision of the unmanned vehicle passing through the traffic intersection, and executing automatic driving.
In the step, a length value N of a matrix MR, an included angle Aggle between roads and positions T of different roads are calculated firstly, wherein the length value N can also represent the number of the roads in the matrix MR, and then a behavior decision of the unmanned vehicles passing through the traffic intersection is generated according to the length value N and the positions T of the different roads.
For example, taking an intersection as an example, if N is 4 and T is 1, a behavior strategy that the unmanned vehicle travels straight along the current road is generated; if N is 4 and T is 0, generating a behavior strategy for the left turn of the unmanned vehicle; if N is 4 and T is 2, generating a behavior strategy for the right turn of the unmanned vehicle; and if N is 4, T is 3 and Aggle is more than 160 degrees and less than or equal to 180 degrees, generating a behavior strategy of turning around the unmanned vehicle.
It can be seen that, according to the scheme provided by the embodiment of the application, when the unmanned vehicle is in the intersection connection area, the vector information corresponding to each road in the road set is obtained by obtaining the road set of the plurality of roads associated with the intersection connection area, and determining included angles between a plurality of roads associated with the intersection connecting area according to the vector information, sequencing the plurality of roads according to the included angles to generate a matrix, wherein a first point connected to the intersection connection area is taken on the center line of each road and a subsequent second point on the center line of each road are formed into a vector, so that, the matrix of a plurality of roads can be obtained by calculating the included angle of the vector, and finally the behavior decision of the unmanned vehicle passing through the traffic intersection is generated by acquiring the corresponding spatial position information of the driving-in road section and the driving-out road section in the matrix, so that after the processing, the algorithm is further simplified, and excessive calculation resources are not occupied.
Corresponding to the embodiment of the application function implementation method, the application also provides an unmanned vehicle control device and an unmanned vehicle for traffic intersection scenes and a corresponding embodiment.
Fig. 4 is a schematic structural diagram of an unmanned vehicle control device for a traffic intersection scene according to an embodiment of the present application.
Referring to fig. 4, the apparatus of the embodiment of the present application includes:
and the planning module 410 is used for planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map, and pre-loading the high-definition map of the specified traffic intersection.
The unmanned vehicle may be an unmanned vehicle and the planning of the path may be based on an autonomous high definition map.
The determining module 420 is configured to, when the unmanned vehicle is located at an intersection connection area of a traffic intersection in the path, obtain vector information corresponding to a plurality of roads associated with the intersection connection area, determine a spatial position relationship between the plurality of roads according to the vector information, and determine a road through which the path passes.
The intersection connecting area can be an area where a plurality of roads in a traffic intersection are connected, and whether the current unmanned vehicle is located in the intersection connecting area of the traffic intersection or not can be searched in data of a high-definition map. The spatial positional relationship may include an angle between the plurality of roads.
And the execution module 430 generates a behavior decision of the unmanned vehicle passing through the traffic intersection, and executes automatic driving according to the behavior decision.
For example, the spatial position information of the entrance road section and the exit road section of the unmanned vehicle at the traffic intersection can be acquired, and the behavior decision of the unmanned vehicle passing through the traffic intersection can be generated according to the spatial position information of the entrance road section and the exit road section.
It can be seen that the device of the embodiment of the application can determine the road where the path passes when the unmanned vehicle is located at the intersection connection area of the traffic intersection, and generates the behavior decision of the unmanned vehicle passing through the traffic intersection according to the spatial position relation. After the treatment, the method for treating the traffic intersection by the unmanned vehicle is simplified, and the implementation is convenient.
Fig. 5 is a schematic structural diagram of an unmanned vehicle control device for a traffic intersection scene according to an embodiment of the present application.
Referring to fig. 5, the determining module 420 of the apparatus of the embodiment of the present application includes:
the location point obtaining sub-module 421 is configured to sequentially obtain at least two location points on the path, and search whether the at least two location points are located in the intersection connection area in the high-definition map.
At least two position points can be taken from the central line of the roads in sequence, and vector information corresponding to each road is obtained according to the at least two position points.
The processing sub-module 422 is configured to, if the location point obtaining sub-module 421 finds that at least two location points are located in the intersection connection area, obtain spatial location information of a road section where the current unmanned vehicle is located according to vector information corresponding to the at least two location points.
The spatial positional relationship may include a position and a direction of a road on a road section where the unmanned vehicle is located.
Therefore, the device of the embodiment simplifies the method for processing the unmanned vehicle passing through the traffic intersection, avoids the defect that the scheme in the related technology adopts a neural network, a vision method, a curve fitting curvature method and other methods to occupy a large amount of computing resources, and is convenient to implement.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 6 is a schematic structural view of an unmanned vehicle according to an embodiment of the present application.
Referring to fig. 6, the drone 600 includes a memory 610 and a processor 620.
The Processor 620 may be a Central Processing Unit (CPU), other 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, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 610 may include various types of storage units such as system memory, Read Only Memory (ROM), and permanent storage. Wherein the ROM may store static data or instructions that are required by the processor 620 or other modules of the computer. The persistent storage device may be a read-write storage device. The persistent storage may 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 persistent storage device employs a mass storage device (e.g., magnetic or optical disk, flash memory) as the persistent storage device. In other embodiments, the permanent storage may be a removable storage device (e.g., floppy disk, optical drive). The system memory may be a read-write memory device or a volatile read-write memory device, such as a dynamic random access memory. The system memory may store instructions and data that some or all of the processors require at runtime. In addition, the memory 610 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic and/or optical disks, as well. In some embodiments, memory 610 may include a removable storage device that is readable and/or writable, such as a Compact Disc (CD), a digital versatile disc read only (e.g., DVD-ROM, dual layer DVD-ROM), a Blu-ray disc read only, an ultra-dense disc, a flash memory card (e.g., SD card, min SD card, Micro-SD card, etc.), a magnetic floppy disk, or the like. Computer-readable storage media do not contain carrier waves or transitory electronic signals transmitted by wireless or wired means.
The memory 610 has stored thereon executable code that, when processed by the processor 620, may cause the processor 620 to perform some or all of the methods described above.
Furthermore, the method according to the present application may also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps of the above-described method of the present application.
Alternatively, the present application may also be embodied as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) having executable code (or a computer program or computer instruction code) stored thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the various steps of the above-described method according to the present application.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. 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 embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. A control method for an unmanned vehicle in a traffic intersection scene, comprising:
planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map, and pre-loading the high-definition map of the appointed traffic intersection;
when the unmanned vehicle is positioned in an intersection connecting area of the traffic intersection, obtaining vector information corresponding to a plurality of roads associated with the intersection connecting area, determining a spatial position relation among the plurality of roads according to the vector information, and determining the road through which the path passes;
and generating a behavior decision of the unmanned vehicle passing through the traffic intersection, and executing automatic driving according to the behavior decision.
2. The method of claim 1, wherein prior to obtaining vector information corresponding to a plurality of roads associated with the intersection connecting region, comprising:
and sequentially taking at least two position points on the path, searching whether the at least two position points are in the intersection connecting area or not in the high-definition map, and if so, obtaining the spatial position information of the road section where the current unmanned vehicle is located according to the vector information corresponding to the at least two position points.
3. The method of claim 1, wherein obtaining vector information corresponding to a plurality of roads associated with the intersection connecting region comprises:
obtaining a set of a plurality of roads associated with the intersection connecting area, sequentially searching the central line of each road in the set, sequentially determining at least two position points on the central line of each road, and obtaining vector information corresponding to each road.
4. The method of claim 1, wherein determining the spatial relationship between the plurality of roads from the vector information comprises:
and determining included angles among a plurality of roads associated with the intersection connecting area according to the vector information.
5. The method of claim 4, wherein determining the included angle between the plurality of roads associated with the intersection connecting region from the vector information comprises:
and sequencing the roads according to the included angles to generate a matrix.
6. The method of claim 5, wherein said determining the road over which the path passes comprises:
obtaining the spatial position information of the driving-in road section of the unmanned vehicle at the traffic intersection and obtaining the spatial position information of the driving-out road section of the unmanned vehicle at the traffic intersection;
and determining the road passed by the path according to the spatial position information of the driving-in road section and the driving-out road section.
7. The method of claim 6, wherein the generating a behavioral decision for the unmanned vehicle to pass through the traffic intersection comprises:
and respectively obtaining the positions and included angles of the driving-in road sections and the driving-out road sections in the matrix, and generating a behavior decision of the unmanned vehicle passing through the traffic intersection according to the positions and the included angles.
8. A control device for an autonomous vehicle for traffic intersection scenarios, comprising:
the planning module is used for planning a path from a starting point to a target point of the unmanned vehicle in the high-definition map and pre-loading the high-definition map of the specified traffic intersection;
the determining module is used for obtaining vector information corresponding to a plurality of roads associated with the intersection connecting area when the unmanned vehicle is positioned in the intersection connecting area of the traffic intersection, determining the spatial position relation among the plurality of roads according to the vector information, and determining the road through which the path passes;
and the execution module is used for generating a behavior decision of the automatic unmanned vehicle passing through the traffic intersection and executing automatic driving according to the behavior decision.
9. The apparatus of claim 8, wherein the determining module comprises:
the position point acquisition sub-module is used for sequentially acquiring at least two position points on the path and searching whether the at least two position points are positioned in the intersection connecting area or not in the high-definition map;
and the processing submodule is used for obtaining the spatial position information of the road section where the current unmanned vehicle is located according to the vector information corresponding to the at least two position points if the at least two position points are located in the intersection connecting area.
10. An unmanned vehicle, comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any one of claims 1-7.
CN202111403857.4A 2021-11-24 2021-11-24 Unmanned vehicle control method and device used in traffic intersection scene and unmanned vehicle Pending CN114103995A (en)

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