CN117008615A - Strategy switching unmanned vehicle track planning method and system - Google Patents
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Abstract
A method and a system for planning a track of an unmanned vehicle based on strategy switching, wherein the method comprises the following steps: 1) Judging whether the current scene is a structured road scene or an unstructured road scene or a escaping scene according to the perception result and the current frame planning result; 2) If the current scene is identified as a structured road scene, a high-precision map generated offline or a map generated in real time according to lane line identification is used for constructing a local route map required by decision planning; otherwise, generating a grid map around the vehicle in real time, projecting the perceived obstacle information to the grid, and constructing a local grid map; 3) According to the identified scene, determining the behavior and state of the vehicle in the current scene; 4) And calling a corresponding track planning method according to the map information and the decision target point information. The method comprises the advantages of a track planning algorithm under a structured road and under an openspace scene, and the unmanned vehicle can cope with more complex abnormal scenes through the real-time switching strategy of scene identification and classification.
Description
Technical Field
The invention relates to a unmanned vehicle track planning method and system, and belongs to the field of intelligent driving.
Background
In trajectory planning, current algorithms rely heavily on the design of the scene (ODD), defining its range of use.
For example, urban scenes, defined as structured roads, rely mainly on track planning methods that generate reference lines based on high-precision maps or lane lines, and once the road is constructed, the structured features disappear, the method will fail completely. For example, a scene of a closed park is generally divided into a structured road area and a parking space area in a map, and different scenes are corresponding to different areas, so that different track planning methods are called. However, in most cases, the road in the closed park is difficult to be represented by the structured road, and more passable areas are lost when the virtual lane lines are forcibly induced, so that the virtual road is completely blocked although the space is actually passable in part of the scene, and the unmanned vehicle cannot escape.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an unmanned vehicle track planning method and system based on strategy switching.
The invention simultaneously comprises the advantages of the track planning algorithm under the structured road and under the openspace scene, and ensures that the unmanned vehicle can cope with more complex abnormal scenes through the real-time switching strategy of scene identification and classification.
The invention discloses a strategy switching-based unmanned vehicle track planning method, which comprises the following specific steps:
step one, judging whether the current scene is a structured road scene or an unstructured road scene according to a perception result and a current frame planning result; if the unmanned vehicle has continuously formed a plurality of frames and cannot normally form an effective planning track, defining the current scene as a getting rid of poverty scene;
if the current scene is identified as a structured road scene, constructing a local route map required by decision planning by using a high-precision map generated offline or a map generated in real time according to lane line identification; if the current scene is an unstructured road scene or a trapping scene, generating a grid map around the vehicle in real time, projecting perceived obstacle information onto the grid, and constructing a local grid map;
and thirdly, deciding the behavior and the state of the vehicle in the current scene according to the scene identified in the previous step. If the road scene is a structured road scene, calculating the action to be executed by the current vehicle through a decision algorithm, wherein the result can be specific target point information or semantic level information; if the road scene is unstructured or the scene is trapped, specific target point information can be given through a decision algorithm, or specific target point information can be given through a cloud, a road end or a remote driver;
calling a corresponding track planning method according to the map information and the decision target point information, and calling a track optimization algorithm based on a road reference line if the track planning method is a structured road scene; if the road scene is unstructured, using an A-type track planning algorithm based on a grid map; if the scene is a getting rid of poverty, a Hybrid A algorithm based on a grid map is used, and finally a safe and comfortable track is output.
Further, the first step of determining the scene of the current vehicle according to the high-precision map information and the perception information specifically comprises the following sub-steps:
step 1.1: inquiring whether a high-precision map of the road section exists according to the current position of the vehicle, judging the difference condition of original information of the current road and the high-precision map according to a perception result if the high-precision map exists, if the difference is not large, indicating that the reference value of the off-line high-precision map is large, driving according to a specific lane, defining a current scene as a structured road scene, if the difference is large, indicating that the current scene has changed greatly, and can not be driven according to the high-precision map information, and defining the current scene as an unstructured road scene;
step 1.2: if the current scene does not have the high-precision map, the lane-level map is generated in real time through sensing lane lines, if the generation is successful, the current scene is defined as a structured road scene, and if the generation is unsuccessful, the current scene is defined as an unstructured road scene.
Step 1.3: if the unmanned vehicle has continuously formed multiple frames and has not normally formed an effective planning track, defining the current scene as a getting rid of poverty scene.
Further, the sensing result in the step one includes lane lines, freespace detecting obstacle and obstacle semantic information.
Further, the second step comprises: according to the scene determined in the step one, constructing a local planning map required by the planning module, which specifically comprises the following steps:
step 2.1: and if the current scene is determined to be the structured road scene in the step one, the current scene is represented as a structured map area with obvious lane information. Obtaining boundary information and road center line information in the structured map region;
step 2.2: sampling map boundary information and central line information generated in the previous step to obtain a discrete upper and lower boundary set and a discrete central line set, thereby forming a local planning map of the structured road scene;
step 2.3: if the scene determined in the first step is an unstructured road scene or a getting rid of poverty scene, the unstructured map with unobvious road features is indicated, or the vehicle in the current state is invalid by using the structured road scene or the planning method under the second step, and getting rid of poverty is needed. Generating a frame-type grid map with a certain distance from front to back and left to right by taking the current position of a vehicle as a center in real time aiming at unstructured road scenes and escape scenes, wherein the grid map is divided into a series of blocks with a certain resolution;
step 2.4: projecting perceived obstacle information onto a grid by representing a square area as 1 if the boundary or interior area of the polygon occupies the square in the grid map, or as 0 otherwise; after the operation, the local grid map in the unstructured road scene and the escape scene is obtained and is a two-dimensional array consisting of 0 and 1.
Still further, the perceived obstacle information described in step 2.4 is in the form of a polygon.
Further, the third step includes: according to the planning map in the corresponding scene and the obstacle information in the current scene generated in the second step, the expected action or state of the vehicle in the current scene is determined, and the method specifically comprises the following steps:
step 3.1: if the current scene is a structured road scene, deciding an expected target point or an expected action in the current state according to a decision algorithm in the prior art; the result can be specific target point information or semantic level information;
step 3.2: if the current scene is an unstructured road scene or a escaping scene, giving a corresponding optimal target point by cloud or road end information preferentially;
step 3.3: if the target point cannot be obtained through the road cloud in the step 3.2, projecting the global navigation path onto the local grid map in a specific projection mode, namely converting the global navigation path coordinate into the local grid map coordinate;
step 3.4: a cost value of each cell in the grid map is calculated, the cost value being composed of a distance cost from the obstacle occupying cell (1 in the two-dimensional array of the map) and a distance cost from the navigation cell (2 in the two-dimensional array).
Furthermore, the target point information in step 3.1 includes a position, an orientation, a speed, an acceleration, etc., and the semantic level information includes driving, lane changing, and parking of the host lane.
Further, the fourth step includes: according to the local planning map information generated in the steps, a track planning method under a corresponding scene is called, and a series of discrete track points are finally output, and the method specifically comprises the following steps:
step 4.1: if the current scene is a structured road scene, generating a track in a drivable area by using a sampling method or an optimizing method by taking a road reference line as a reference:
step 4.2: if the current scene is an unstructured road scene, adopting an A algorithm aiming at the generated grid cost map, searching out a path with the minimum cost, distributing proper speed, and finally outputting as a current frame track;
step 4.3: if the current scene is a getting rid of poverty scene, a Hybrid A algorithm is adopted for the generated grid cost map, a path which accords with vehicle dynamics and possibly comprises reversing operation is searched, proper speed is distributed, and finally a getting rid of poverty track is output.
A second aspect of the present invention relates to an unmanned vehicle trajectory planning system based on strategic switching, comprising:
the current scene recognition module is used for judging whether the current scene is a structured road scene or an unstructured road scene according to a perception result (including lane lines, freespace detection obstacles, obstacle semantic information and the like) and a current frame planning result; if the unmanned vehicle has continuously formed a plurality of frames and cannot normally form an effective planning track, defining the current scene as a getting rid of poverty scene;
the map production module required by decision planning is used for constructing a local route map required by decision planning by using a high-precision map generated offline or a map generated in real time according to lane line identification if the current scene is identified as a structured road scene; if the current scene is an unstructured road scene or a trapping scene, generating a grid map around the vehicle in real time, projecting perceived obstacle information onto the grid, and constructing a local grid map;
the vehicle behavior and state decision module in the current scene decides the vehicle behavior and state in the current scene according to the other scenes; if the road scene is a structured road scene, calculating the action to be executed by the current vehicle through a decision algorithm, wherein the result can be specific target point information (including position, orientation, speed, acceleration and the like) or semantic level information (including driving on a lane, changing lanes, stopping and the like); if the road scene is unstructured or the scene is trapped, specific target point information can be given through a decision algorithm, or specific target point information can be given through a cloud, a road end or a remote driver;
the track planning method calling module calls a corresponding track planning method according to the map information and the decision target point information, and calls a track optimization algorithm based on a road reference line if the track planning method is a structured road scene; if the road scene is unstructured, using an A-type track planning algorithm based on a grid map; if the scene is a getting rid of poverty, a Hybrid A algorithm based on a grid map is used, and finally a safe and comfortable track is output.
A third aspect of the present invention relates to a policy switching-based vehicle trajectory planning device, which includes a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for implementing the policy switching-based vehicle trajectory planning method according to the present invention when executing the executable codes
A fourth aspect of the present invention relates to a computer readable storage medium, wherein a program is stored thereon, which when executed by a processor, implements a policy switching-based unmanned vehicle trajectory planning method of the present invention.
The working principle of the invention is as follows:
firstly, classifying a current scene through a scene recognition module and a current planning state of a vehicle; then constructing a local planning map under the current scene according to the specific classification scene, and calling different decision strategies so as to form an action sequence to be executed by the vehicle; and finally, calling a corresponding track planning method by the corresponding scene and action sequence, and outputting a specific track executable by the vehicle. Through classifying scenes and calling different decision strategies, the algorithm can well solve the problem that the offline high-precision map and the real-time environment change are large, and meanwhile, by utilizing a strategy switching mode, the unmanned vehicle can be automatically trapped even if being in dilemma, so that the robustness of the algorithm is greatly improved.
The innovation points of the invention are as follows:
firstly, a mode for switching track planning strategies according to real-time scenes is provided, and the method can adapt to scenes in which an offline map is not available due to road construction and other reasons; secondly, under the framework, the track escaping strategy of the automatic driving vehicle can be realized at the same time, and the robustness of the algorithm is ensured; finally, the efficiency and the path-finding quality of the search algorithm are improved in a mode that the navigation path is projected to the local grid map.
The invention has the advantages that:
the current automatic driving function is not dependent on a high-precision map, and a new local map can be still constructed in real time and track planning is completed under the condition that an offline map is unavailable or has large difference with an actual scene, so that the applicable scene and capability of an algorithm are greatly improved; the calculation efficiency and the solving quality of the trajectory planning algorithm under the condition of no high-precision map are improved.
Drawings
FIG. 1 is a flowchart of a method for trajectory planning based on policy switching, according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a structured road scene provided by an embodiment of the present invention;
FIG. 3 is a schematic view of an unstructured road scene provided by an embodiment of the present invention;
FIG. 4 is a schematic view of a grid map provided in an embodiment of the present invention;
fig. 5 is a schematic diagram of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and technical effects of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples of the specification.
Example 1
In order to achieve the purpose that an unmanned vehicle handles complex abnormal scenes, the specific embodiment of the invention identifies the current scene through a perception result and current frame planning, if the current scene is a structured road scene, a high-precision map generated offline or a map generated in real time according to lane line identification is used for constructing a local route map required by decision planning, actions required to be executed by the current vehicle are calculated through a decision algorithm, a track optimization algorithm based on road reference lines is called, if the current scene is an unstructured road scene or a getting rid of the trapped scene, perceived obstacle information is projected onto a grid, a local grid map is constructed in real time, specific target point information is given through a decision algorithm, a cloud, a road section or a remote driver, a track planning algorithm based on the grid map is called, and finally a safe and comfortable track is output.
The embodiment of the invention provides an unmanned vehicle track planning method based on strategy switching, which is shown in fig. 1 and comprises the following steps:
and step 1, judging whether the current scene is a structured road scene or an unstructured road scene according to a perception result (including lane lines, fresh detection obstacles, obstacle semantic information and the like) and a current frame planning result. If the unmanned vehicle has continuously formed a plurality of frames and fails to normally form an effective planning track, defining the current scene as a getting rid of poverty scene, and specifically comprising the following steps:
step 1.1: inquiring whether a high-precision map of the road section exists according to the current position of the vehicle, judging the difference condition of original information of the current road and the high-precision map according to a perception result if the high-precision map exists, if the difference is not large, indicating that the reference value of the off-line high-precision map is large, driving according to a specific lane, defining a current scene as a structured road scene, as shown in fig. 2, if the difference is large, indicating that the current scene has changed greatly, and can not drive according to the high-precision map information, and defining the current scene as an unstructured road scene, as shown in fig. 3.
Step 1.2: if the current scene does not have the high-precision map, the lane-level map is generated in real time through sensing lane lines, if the generation is successful, the current scene is defined as a structured road scene, and if the generation is unsuccessful, the current scene is defined as an unstructured road scene.
Step 1.3: if the unmanned vehicle has continuously formed multiple frames and has not normally formed an effective planning track, defining the current scene as a getting rid of poverty scene.
Step 2, constructing a local planning map required by the planning module according to the scene determined in the step 1, wherein the specific steps are as follows:
and 2.1, if the current scene is determined to be the structured road scene in the first step, the current scene is a structured map area with obvious lane information. Boundary information and road centerline information in the structured map region are obtained.
Step 2.2, mapping boundary information and centerline information generated in the previous stepSampling information to obtain a discrete upper and lower bound set: lb (lb) 0 ,lb 1 ,…,lb n-1 、{ub 0 ,ub 1 ,…,ub n-1 -and a discrete set of centerlines: thereby composing a local planning map of the structured road scene.
And 2.3, if the scene determined in the first step is an unstructured road scene or a getting rid of poverty scene, indicating that the current unstructured map is an unstructured road scene with unobvious road characteristics, or that the vehicle is invalid by using the structured road scene or a planning method under the second step in the current state, and getting rid of poverty is needed. For unstructured road scenes and escape scenes, generating frame-type grid maps with the current position of the vehicle as the center and a certain distance from front to back and from left to right in real time, wherein the grid maps are divided into a series of blocks with a certain resolution.
Step 2.4, projecting the perceived obstacle information (generally in the form of a polygon) onto the grid by representing a square area as 1 if the boundary or interior area of the polygon occupies a square in the grid map, or as 0 otherwise. After the operation, the local grid map in the unstructured road scene and the escape scene is obtained and is a two-dimensional array consisting of 0 and 1.
Step 3, deciding the expected action or state of the vehicle in the current scene according to the planning map in the corresponding scene and the obstacle information in the current scene, wherein the specific steps are as follows:
and 3.1, if the current scene is a structured road scene, deciding an expected target point or an expected action in the current state according to a decision algorithm in the prior art. The result may be specific target point information (including position, orientation, speed, acceleration, etc.), or semantic level information (including own lane driving, lane changing, parking, etc.).
Step 3.2, if the current scene is an unstructured road scene or a escaping scene, giving a corresponding optimal target point by cloud or road end information preferentially.
And 3.3, if the target point cannot be acquired through the path cloud in the step 3.2, projecting the global navigation path onto the local grid map in a specific projection mode, namely converting the global navigation path coordinate into the local grid map coordinate, and setting the value of the corresponding grid in the corresponding two-dimensional array to be 2, wherein the grid map is shown in fig. 4.
Step 3.4, calculating a cost value of each cell in the grid map, wherein the cost value is expressed as:
wherein C is ij Representing the total cost of the ith row and jth column grid,representing the distance cost from the obstacle occupying grid (1 in the two-dimensional array of the map) as shown in the following equation:
wherein x is ij Representing the x coordinate, y of the ith row and jth column grid ij Representing the y-coordinate of the ith row and jth column grid. X is x obs·ij Representing the x coordinate, y of the nearest barrier grid to the ith row and jth column grid obs·ij The y-coordinate representing the nearest obstacle grid to the ith row and jth column grid is obtained by traversing the obstacle grid.
Representing the distance cost from the navigation grid (2 in the two-dimensional array). The following formula is shown:
wherein x is r·ij Representing the x coordinate, y of the nearest navigation grid to the ith row and jth column grid r·ij Representing the y-coordinate of the closest navigation grid to the ith row and jth column grid.
And 4, calling a track planning method under a corresponding scene according to the local planning map information generated in the step, and finally outputting a series of discrete track points, wherein the specific steps are as follows:
and 4.1, if the current scene is a structured road scene, generating a track in a drivable area by using a sampling method or an optimizing method with a road reference line as a reference.
And 4.2, if the current scene is an unstructured road scene, searching a path with the minimum cost by adopting an A-scale algorithm aiming at the generated grid cost map, distributing proper speed, and finally outputting the path as the current frame track.
And 4.3, if the current scene is a getting rid of poverty scene, adopting a Hybrid A algorithm aiming at the generated grid cost map, searching a path which accords with vehicle dynamics and possibly comprises reversing operation, distributing proper speed, and finally outputting a getting rid of poverty track.
Example 2
Referring to fig. 5, the present embodiment provides an unmanned vehicle track planning system based on policy switching, so as to implement the unmanned vehicle track planning method based on policy switching described in embodiment 1, including:
the current scene recognition module is used for judging whether the current scene is a structured road scene or an unstructured road scene according to a perception result (including lane lines, freespace detection obstacles, obstacle semantic information and the like) and a current frame planning result; if the unmanned vehicle has continuously formed a plurality of frames and cannot normally form an effective planning track, defining the current scene as a getting rid of poverty scene;
the map production module required by decision planning is used for constructing a local route map required by decision planning by using a high-precision map generated offline or a map generated in real time according to lane line identification if the current scene is identified as a structured road scene; if the current scene is an unstructured road scene or a trapping scene, generating a grid map around the vehicle in real time, projecting perceived obstacle information onto the grid, and constructing a local grid map;
the vehicle behavior and state decision module in the current scene decides the vehicle behavior and state in the current scene according to the other scenes; if the road scene is a structured road scene, calculating the action to be executed by the current vehicle through a decision algorithm, wherein the result can be specific target point information (including position, orientation, speed, acceleration and the like) or semantic level information (including driving on a lane, changing lanes, stopping and the like); if the road scene is unstructured or the scene is trapped, specific target point information can be given through a decision algorithm, or specific target point information can be given through a cloud, a road end or a remote driver;
the track planning method calling module calls a corresponding track planning method according to the map information and the decision target point information, and calls a track optimization algorithm based on a road reference line if the track planning method is a structured road scene; if the road scene is unstructured, using an A-type track planning algorithm based on a grid map; if the scene is a getting rid of poverty, a Hybrid A algorithm based on a grid map is used, and finally a safe and comfortable track is output.
Example 3
The present embodiment provides a computer-readable storage medium having a program stored thereon, which when executed by a processor, implements the method for policy switching-based vehicle trajectory planning described in embodiment 1.
Example 4
The embodiment provides an unmanned vehicle track planning device based on strategy switching, which comprises a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the unmanned vehicle track planning method based on strategy switching described in the embodiment 1 when executing the executable codes.
At the hardware level, the unmanned vehicle track planning device based on strategy switching comprises a processor, an internal bus, a network interface, a memory and a nonvolatile memory, and can also comprise hardware required by other businesses. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement the method described above with respect to fig. 1. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present invention, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
Improvements to one technology can clearly distinguish between improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) and software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (11)
1. A strategy switching-based unmanned vehicle track planning method comprises the following steps:
step one, judging whether the current scene is a structured road scene or an unstructured road scene according to a perception result and a current frame planning result; if the unmanned vehicle has continuously formed a plurality of frames and cannot normally form an effective planning track, defining the current scene as a getting rid of poverty scene;
if the current scene is identified as a structured road scene, constructing a local route map required by decision planning by using a high-precision map generated offline or a map generated in real time according to lane line identification; if the current scene is an unstructured road scene or a trapping scene, generating a grid map around the vehicle in real time, projecting perceived obstacle information onto the grid, and constructing a local grid map;
and thirdly, deciding the behavior and the state of the vehicle in the current scene according to the scene identified in the previous step. If the road scene is a structured road scene, calculating the action to be executed by the current vehicle through a decision algorithm, wherein the result can be specific target point information or semantic level information; if the road scene is unstructured or the scene is trapped, specific target point information can be given through a decision algorithm, or specific target point information can be given through a cloud, a road end or a remote driver;
calling a corresponding track planning method according to the map information and the decision target point information, and calling a track optimization algorithm based on a road reference line if the track planning method is a structured road scene; if the road scene is unstructured, using an A-type track planning algorithm based on a grid map; if the scene is a getting rid of poverty, a Hybrid A algorithm based on a grid map is used, and finally a safe and comfortable track is output.
2. The method for planning a trajectory of an unmanned vehicle based on strategic switching of claim 1, wherein step one comprises: determining a scene of the current vehicle according to the high-precision map information and the perception information, wherein the method specifically comprises the following substeps:
step 1.1: inquiring whether a high-precision map of the road section exists according to the current position of the vehicle, judging the difference condition of original information of the current road and the high-precision map according to a perception result if the high-precision map exists, if the difference is not large, indicating that the reference value of the off-line high-precision map is large, driving according to a specific lane, defining a current scene as a structured road scene, if the difference is large, indicating that the current scene has changed greatly, and can not be driven according to the high-precision map information, and defining the current scene as an unstructured road scene;
step 1.2: if the current scene does not have the high-precision map, the lane-level map is generated in real time through sensing lane lines, if the generation is successful, the current scene is defined as a structured road scene, and if the generation is unsuccessful, the current scene is defined as an unstructured road scene.
Step 1.3: if the unmanned vehicle has continuously formed multiple frames and has not normally formed an effective planning track, defining the current scene as a getting rid of poverty scene.
3. The method of claim 1, wherein the sensing result in the first step includes lane lines, a freepace detection obstacle, and obstacle semantic information.
4. The method for planning a trajectory of an unmanned vehicle based on strategy switching of claim 1, wherein step two comprises: according to the scene determined in the step one, constructing a local planning map required by the planning module, which specifically comprises the following steps:
step 2.1: and if the current scene is determined to be the structured road scene in the step one, the current scene is represented as a structured map area with obvious lane information. Obtaining boundary information and road center line information in the structured map region;
step 2.2: sampling map boundary information and central line information generated in the previous step to obtain a discrete upper and lower boundary set and a discrete central line set, thereby forming a local planning map of the structured road scene;
step 2.3: if the scene determined in the first step is an unstructured road scene or a getting rid of poverty scene, the unstructured map with unobvious road features is indicated, or the vehicle in the current state is invalid by using the structured road scene or the planning method under the second step, and getting rid of poverty is needed. Generating a frame-type grid map with a certain distance from front to back and left to right by taking the current position of a vehicle as a center in real time aiming at unstructured road scenes and escape scenes, wherein the grid map is divided into a series of blocks with a certain resolution;
step 2.4: projecting perceived obstacle information onto a grid by representing a square area as 1 if the boundary or interior area of the polygon occupies the square in the grid map, or as 0 otherwise; after the operation, the local grid map in the unstructured road scene and the escape scene is obtained and is a two-dimensional array consisting of 0 and 1.
5. A method of planning a trajectory of an unmanned vehicle based on strategic switching in accordance with claim 1, wherein said perceived obstacle information of step 2.4 is in the form of polygons.
6. The method for planning a trajectory of an unmanned vehicle based on strategic switching of claim 1, wherein step three comprises: according to the planning map in the corresponding scene and the obstacle information in the current scene generated in the second step, the expected action or state of the vehicle in the current scene is determined, and the method specifically comprises the following steps:
step 3.1: if the current scene is a structured road scene, deciding an expected target point or an expected action in the current state according to a decision algorithm in the prior art; the result can be specific target point information or semantic level information;
step 3.2: if the current scene is an unstructured road scene or a escaping scene, giving a corresponding optimal target point by cloud or road end information preferentially;
step 3.3: if the target point cannot be obtained through the road cloud in the step 3.2, projecting the global navigation path onto the local grid map in a specific projection mode, namely converting the global navigation path coordinate into the local grid map coordinate;
step 3.4: a cost value of each cell in the grid map is calculated, the cost value being composed of a distance cost from the obstacle occupying cell (1 in the two-dimensional array of the map) and a distance cost from the navigation cell (2 in the two-dimensional array).
7. The method for planning a trajectory of an unmanned vehicle based on strategic switching of claim 6, wherein the information of the target point in step 3.1 includes position, orientation, speed, and acceleration, and the semantic level information includes driving, lane changing, and parking.
8. The method for planning a trajectory of an unmanned vehicle based on strategic switching of claim 1, wherein step four comprises: according to the local planning map information generated in the steps, a track planning method under a corresponding scene is called, and a series of discrete track points are finally output, and the method specifically comprises the following steps:
step 4.1: if the current scene is a structured road scene, generating a track in a drivable area by using a sampling method or an optimizing method by taking a road reference line as a reference:
step 4.2: if the current scene is an unstructured road scene, adopting an A algorithm aiming at the generated grid cost map, searching out a path with the minimum cost, distributing proper speed, and finally outputting as a current frame track;
step 4.3: if the current scene is a getting rid of poverty scene, a Hybrid A algorithm is adopted for the generated grid cost map, a path which accords with vehicle dynamics and possibly comprises reversing operation is searched, proper speed is distributed, and finally a getting rid of poverty track is output.
9. An unmanned vehicle track planning system based on strategy switching is characterized in that: comprising the following steps:
the current scene recognition module is used for judging whether the current scene is a structured road scene or an unstructured road scene according to the perception result and the current frame planning result; if the unmanned vehicle has continuously formed a plurality of frames and cannot normally form an effective planning track, defining the current scene as a getting rid of poverty scene;
the map production module required by decision planning is used for constructing a local route map required by decision planning by using a high-precision map generated offline or a map generated in real time according to lane line identification if the current scene is identified as a structured road scene; if the current scene is an unstructured road scene or a trapping scene, generating a grid map around the vehicle in real time, projecting perceived obstacle information onto the grid, and constructing a local grid map;
the vehicle behavior and state decision module in the current scene decides the vehicle behavior and state in the current scene according to the other scenes; if the road scene is a structured road scene, calculating the action to be executed by the current vehicle through a decision algorithm, wherein the result can be specific target point information or semantic level information; if the road scene is unstructured or the scene is trapped, specific target point information can be given through a decision algorithm, or specific target point information can be given through a cloud, a road end or a remote driver;
the track planning method calling module calls a corresponding track planning method according to the map information and the decision target point information, and calls a track optimization algorithm based on a road reference line if the track planning method is a structured road scene; if the road scene is unstructured, using an A-type track planning algorithm based on a grid map; if the scene is a getting rid of poverty, a Hybrid A algorithm based on a grid map is used, and finally a safe and comfortable track is output.
10. An unmanned vehicle trajectory planning device based on strategy switching, comprising a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the unmanned vehicle trajectory planning method based on strategy switching according to one of claims 1-8 when executing the executable codes.
11. A computer readable storage medium, having stored thereon a program which, when executed by a processor, implements a policy switching based unmanned vehicle trajectory planning method as claimed in any one of claims 1 to 8.
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| CN117870715A (en) * | 2024-03-13 | 2024-04-12 | 上海鉴智其迹科技有限公司 | Map switching method and device, electronic equipment and storage medium |
| CN117870715B (en) * | 2024-03-13 | 2024-05-31 | 上海鉴智其迹科技有限公司 | Map switching method and device, electronic equipment and storage medium |
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