CN111982143A - Vehicle and vehicle path planning method and device - Google Patents
Vehicle and vehicle path planning method and device Download PDFInfo
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- 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
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Abstract
The application discloses a vehicle and a vehicle path planning method and device, wherein the method comprises the following steps: acquiring sensing data and map positioning information of the barrier; predicting to obtain a predicted track of the obstacle within a preset time length according to the sensing data and the map positioning information; and generating a plurality of path points according to the vehicle information of the vehicle and the predicted path in the preset time length so as to generate a vehicle driving path. Therefore, the problem of path planning of dynamic obstacles or dynamic and static obstacles simultaneously is solved by fully considering the state and constraint limits of the vehicle, real-time effective dynamic obstacle avoidance is guaranteed, and safe driving is realized.
Description
Technical Field
The present disclosure relates to the field of vehicle technologies, and in particular, to a vehicle and a method and an apparatus for vehicle path planning.
Background
At present, automatic driving of a vehicle generally follows a planned path, and when a dynamic obstacle appears on the planned path, the vehicle needs to re-plan a local path according to actual conditions to realize dynamic obstacle avoidance.
In the related art, for a static obstacle or a static complex environment, obstacle information (shape, position, etc.) and environment information are generally abstracted into a mathematical model for calculation; and for the dynamic obstacle, applying the static model to predict the obstacle.
However, when the path planning method in the related art is used, the feasibility of the planned path and the driving safety cannot be guaranteed, the calculation amount is large, the efficiency is low, the real-time performance cannot be guaranteed, and the method is difficult to implement when being applied to a dynamic obstacle, and a solution is urgently needed.
Content of application
The application provides a vehicle and a vehicle path planning method and device, which solve the problem of path planning of dynamic obstacles or dynamic and static obstacles at the same time by fully considering the state and constraint limits of the vehicle, ensure real-time effective dynamic obstacle avoidance and realize safe driving.
An embodiment of a first aspect of the present application provides a vehicle path planning method, including the following steps:
acquiring sensing data and map positioning information of the barrier;
predicting to obtain a predicted track of the obstacle within a preset time length according to the perception data and the map positioning information; and
and generating a plurality of path points according to the vehicle information of the vehicle and the predicted path in the preset time length so as to generate a vehicle running path.
Optionally, after generating the plurality of waypoints, the method further includes:
smoothing the path points, and obtaining a plurality of path points which do not meet preset conditions by screening based on maximum curvature constraint;
and connecting the remaining route points to generate the vehicle driving route.
Optionally, the predicting the predicted trajectory of the obstacle within a preset time according to the perception data and the map positioning information includes:
generating current time state data of the barrier according to the perception data and the map positioning information;
and predicting to obtain the state data of the barrier at the next moment according to the state data at the current moment to obtain the predicted track at the next moment so as to obtain the predicted track within the preset time length.
Optionally, the generating current-time state data of the obstacle according to the perception data and the map positioning information includes:
and obtaining the action intention of the next moment by utilizing a time series model according to the action states of the dynamic barrier in the environment at the previous moment and the current moment.
Optionally, the estimating, according to the current time state data, to obtain next time state data of the obstacle, and obtain a predicted trajectory at the next time, so as to obtain the predicted trajectory within the preset time length, includes:
and gradually predicting the behavior intention of each dynamic obstacle at the next moment by using the incremental prediction model to form a new environment model, and sequentially increasing to obtain the predicted track within the preset time length.
Optionally, the acquiring sensing data and mapping information of the obstacle includes:
acquiring a pre-drawn global planning map, wherein the global planning map comprises a lane topological relation and geometric information;
acquiring the motion information of the obstacle, wherein the motion information comprises a geometric state, position information, speed information, acceleration information and/or course information;
and mapping the motion information into a coordinate system of the global planning map by a vehicle body coordinate system.
An embodiment of a second aspect of the present application provides a vehicle path planning apparatus, including:
the acquisition module is used for acquiring sensing data of obstacles and map positioning information;
the prediction module is used for predicting to obtain a predicted track of the obstacle within a preset time length according to the perception data and the map positioning information; and
and the generating module is used for generating a plurality of path points according to the vehicle information of the vehicle and the predicted path in the preset time length so as to generate a vehicle running path.
Optionally, after generating the plurality of waypoints, the generating module is further configured to:
smoothing the path points, and obtaining a plurality of path points which do not meet preset conditions by screening based on maximum curvature constraint;
and connecting the remaining route points to generate the vehicle driving route.
Optionally, the prediction module is specifically configured to:
generating current time state data of the barrier according to the perception data and the map positioning information;
and predicting to obtain the state data of the barrier at the next moment according to the state data at the current moment to obtain the predicted track at the next moment so as to obtain the predicted track within the preset time length.
Optionally, the prediction module is further configured to:
and obtaining the action intention of the next moment by utilizing a time series model according to the action states of the dynamic barrier in the environment at the previous moment and the current moment.
Optionally, the prediction module is further configured to:
and gradually predicting the behavior intention of each dynamic obstacle at the next moment by using the incremental prediction model to form a new environment model, and sequentially increasing to obtain the predicted track within the preset time length.
Optionally, the acquisition module is specifically configured to:
acquiring a pre-drawn global planning map, wherein the global planning map comprises a lane topological relation and geometric information;
acquiring the motion information of the obstacle, wherein the motion information comprises a geometric state, position information, speed information, acceleration information and/or course information;
and mapping the motion information into a coordinate system of the global planning map by a vehicle body coordinate system.
An embodiment of a third aspect of the present application provides a vehicle, which includes the vehicle path planning apparatus described above.
An embodiment of a fourth aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a vehicle path planning method as described in the above embodiments.
In a fifth aspect, embodiments of the present application provide a computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the vehicle path planning method according to the foregoing embodiments.
The motion information of the obstacles is mapped into a coordinate system of the global planning map through a vehicle body coordinate system, the action intention at the next moment is predicted according to the historical behavior and the current state of the obstacles, the action intention at the next moment of each dynamic obstacle is gradually predicted, a new environment model is formed, the new environment model is sequentially increased in number to obtain the predicted track within the preset time length, therefore, the motion tracks of the obstacles are collected, reasonable local path points are planned, and the path points are processed in multiple steps to obtain the optimal path. Therefore, the state and constraint limitation of the vehicle are fully considered, the feasibility of the planned path is greatly improved based on environment modeling, the driving safety is improved, and the problem of path planning of dynamic obstacles or dynamic and static obstacles is solved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a vehicle path planning method according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a vehicle path planning system according to an embodiment of the present application;
FIG. 3 is a flow chart of a vehicle path planning method according to one embodiment of the present application;
FIG. 4 is an exemplary diagram of a vehicle path planning apparatus according to an embodiment of the present application;
FIG. 5 is a block schematic diagram of a vehicle according to an embodiment of the present application;
fig. 6 is an exemplary diagram of an electronic device according to an embodiment of the application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes a vehicle and a vehicle path planning method and device according to an embodiment of the present application with reference to the drawings.
Specifically, fig. 1 is a schematic flow chart of a vehicle path planning method provided in an embodiment of the present application.
As shown in fig. 1, the vehicle path planning method includes the following steps:
in step S101, sensing data of an obstacle and map positioning information are collected.
It is understood that the obstacles may include dynamic obstacles, static obstacles, dynamic obstacles, and static obstacles, wherein the dynamic obstacles may be: a pedestrian, a motor vehicle, or a non-motor vehicle; the static obstacle may be: curbs, trees, road signs, driveways or other obstacles, etc. The sensory data of the obstacle may comprise the appearance, shape and/or position, etc. of a dynamic obstacle, as well as the appearance, shape and/or position, etc. of a static obstacle; map location information is a type of geographic coordinate data that may be used to determine the location of work orders and to add location markers to a map.
Therefore, the sensing module (such as an environment sensor) can be used for acquiring sensing data of the obstacle, wherein the environment sensor can be, but is not limited to, an image acquisition device, an infrared detector, a pressure sensor, a laser radar, an ultrasonic radar and the like, and can detect light, heat, sound, pressure or other variables for monitoring the state of the vehicle. For example, the embodiment of the application can collect the position of a lane line of a driving lane of a vehicle through an image collector, detect the distance between the vehicle and a front vehicle and a rear vehicle through a radar, detect whether a pedestrian exists in front of the vehicle and the distance between the pedestrian and the infrared detector, and judge whether the vehicle is collided with by a pressure sensor. The information such as lane line position, distance between the front and rear vehicles, pedestrian distance, whether collision occurs and the like is the sensing data of the obstacle. The embodiment of the application can acquire the map positioning information through the map positioning module.
Optionally, in some embodiments, the acquiring sensing data and mapping information of the obstacle includes: acquiring a pre-drawn global planning map, wherein the global planning map comprises a lane topological relation and geometric information; acquiring the motion information of the obstacle, wherein the motion information comprises a geometric state, position information, speed information, acceleration information and/or course information; and mapping the motion information into a coordinate system of the global planning map from the vehicle body coordinate system.
Specifically, the embodiment of the present application may obtain a pre-drawn good planning map including a lane topological relation and a geometric information office, where the topological relation refers to a mutual relation among spatial data satisfying a topological geometry principle, that is, an adjacency, association, inclusion, and communication relation between entities represented by nodes, arc segments, and polygons, such as: the relationship of the dots to the adjacent dots, the relationship of the dots to the surface, the relationship of the lines to the surface, the relationship of the surfaces to the surface, and the like.
Then, acquiring the movement information of the obstacle through a sensing module, wherein the obstacle can comprise a dynamic obstacle and a static obstacle, and the movement information can comprise any one or more of a geometric state, position information, speed information, acceleration information and course information;
and finally, integrating the collected map positioning information and the movement information of the obstacles, so that the obstacle information is mapped into a global planning map coordinate system by a vehicle body coordinate system.
From this, through the perception data of receiving and gathering vehicle perception module, the information that map orientation module gathered to abstract calculation, with motion information by the vehicle body coordinate system to the coordinate system of global planning map, thereby the planning route of the later stage of being convenient for makes the feasibility of planning the route promote greatly, has effectively guaranteed the security of driving.
In step S102, a predicted trajectory of the obstacle within a preset time period is predicted according to the sensing data and the map positioning information.
Optionally, in some embodiments, predicting a predicted trajectory of the obstacle within a preset time period according to the perception data and the map positioning information includes: and generating current time state data of the barrier according to the sensing data and the map positioning information.
Optionally, in some embodiments, generating the current-time state data of the obstacle according to the perception data and the map positioning information includes: and obtaining the action intention of the next moment by utilizing a time series model according to the action states of the dynamic barrier in the environment at the previous moment and the current moment. The time series model can predict the state at the next moment based on the historical state data and the current moment state data.
It can be understood that, assuming that the behavior state at the current time t is X and the previous time of the dynamic obstacle in the environment is t-p, t-p +1, …, t-1 in the embodiment of the present application, the time series model X can be usedt+1＝f(Xt,xt-1,…,Xt-p) And calculating the action intention of the next moment and the following moments, wherein the action intention can be whether the obstacle accelerates, decelerates, turns or the like at the next moment.
It should be noted that the behavior prediction of the obstacle through the time sequence model is only exemplary, and those skilled in the art may also perform prediction through other models, such as a self-heuristic model, reinforcement learning, and the like, and may predict a possible behavior by setting a corresponding cost function, where the difference is that the reinforcement learning needs to train the model in advance to obtain a suitable cost function, and details are not described here to avoid redundancy.
Therefore, the time series model is established by combining the perception data, the map positioning information, the historical state of the dynamic barrier and the current state information, the possible behavior of the barrier at the next moment is calculated, and the calculation efficiency is effectively improved.
And predicting to obtain the state data of the obstacle at the next moment according to the state data at the current moment to obtain the predicted track at the next moment so as to obtain the predicted track within the preset time length.
Optionally, in some embodiments, estimating, according to the current time state data, state data of the obstacle at a next time to obtain a predicted trajectory at the next time, so as to obtain the predicted trajectory within a preset time duration, includes: and gradually predicting the behavior intention of each dynamic obstacle at the next moment by using the incremental prediction model to form a new environment model, and sequentially increasing to obtain a predicted track within a preset time length. The incremental prediction model is based on the predicted track of the next moment, and after the environment model is updated, the incremental iteration is carried out step by step to obtain the predicted track of a longer time.
Specifically, assuming that the interaction of behavioral intentions between dynamic obstacles is negligible, the embodiment of the present application may gradually predict the state of each dynamic obstacle at the next time (e.g., after 1 second) by using an incremental prediction model, so as to form a new environment model, and sequentially increment the model, thereby obtaining the motion trajectory of the dynamic obstacle in a longer time period.
When the obstacle trajectory is predicted, it is preferable, but not limited to, to use an incremental prediction model, and the predicted trajectory may be mainly based on the trajectory of the lane (1D) (the amount of calculation is reduced) or may be based on the environment (2D).
In step S103, a plurality of path points are generated according to the own vehicle information of the vehicle and the predicted path within the preset time length to generate a vehicle driving path.
It can be understood that, in the embodiment of the present application, an optimized and feasible path may be calculated according to the predicted trajectory within the preset time duration acquired in step S102 and the vehicle information, and path planning may be performed according to a fixed frequency, and relevant data is output and path points are generated, so as to generate a vehicle driving path.
In order to make those skilled in the art further understand how to generate a plurality of route points according to the vehicle information of the vehicle and the predicted trajectory within the preset time period to generate the vehicle driving route, the following takes an improved RRT algorithm as an example to describe in detail. Wherein the improvement point is the selection of the terminal node and the selection of the measurement function.
Specifically, the RRT algorithm: starting state q in state space C0＝qstartRandomly constructing a search tree T for a root node, setting a target deviation probability threshold value P, and selecting a node q by iterative random samplingrandomRandomly obtaining a probability value p according to the uniform probability distribution during random sampling, if p is>P, then generate node qrandomOtherwise qrandom＝qgoal. Traversing T to find q awayrandomNearest node qnearFrom node qnearTo qrandomExtending a distance to obtain a new node qnewIf q isnewGiving up the expansion when colliding with the obstacle, otherwise, connecting the node qnewAdded to the tree. Repeating the above steps to qnearAnd q isgoalThe distance is smaller than a set threshold (the random tree is considered to reach a target point), or exceeds a set maximum iteration number, and the process is finished;
selecting a final node: different from the choice of qnearAnd q isgoalEnding when the distance is smaller than the set threshold value, and expanding once more to enable the target point to be in the connecting line range of the terminal node of the tree T and the previous node (to ensure the target point to be on the planned path);
different from the basic RRT algorithm, the Euclidean distance is adopted as a measurement function, and the adjacent node q is consideredi、qjThe measurement function C (q) more suitable for the application of the intelligent vehicle is selectedi，qj) Namely:
wherein (x)i,yi)、(xj,yj) Are respectively adjacent nodes qi、qjCoordinates in the vehicle body coordinate system, θiIs a node qiAngle with an adjacent node thereon, N1(d)、N2And (theta) is a normalized function of the distance and the angle respectively, and alpha and beta are weight parameters.
It should be noted that the above-mentioned manner of generating multiple waypoints through the optimized RRT algorithm is only exemplary, and there are other manners in the art to generate multiple waypoints, for example, for other optimization manners of the RRT algorithm, such as multi-order polynomial + cost function + collision detection, etc., and details are not described here in order to avoid redundancy. Optionally, in some embodiments, after generating the plurality of waypoints, the method further includes: smoothing the multiple path points, and obtaining and screening the multiple path points which do not meet the preset conditions based on the maximum curvature constraint; the remaining plurality of route points are connected to generate a vehicle travel route.
Wherein, path planning-smoothing post-processing: for the generated path points, deleting the path points based on the maximum curvature constraint (the deleting aims to reduce the calculated amount, if the path points are too many, the calculating efficiency is reduced, the resource waste is caused, and the real-time performance is influenced); and then, utilizing cubic B-spline curve smoothing to the new path point, and updating the path.
Specifically, the embodiment of the present application may sequentially connect subsequent path points from a first node/path point (initial state) to the path points generated in step S103, delete nodes between the nodes if the connection line between the nodes does not intersect with the obstacle path, sequentially push to the intersection generation (collision generation) of the connection line and the obstacle path, and repeat the above steps until the last node (target state) is reached, with the previous node in collision as a new starting point.
Setting the threshold value phi of the included angleminFor the above generated path point connecting lines, if the included angle phi between the adjacent connecting linesi<φminBased on phi between two nodesminInserting a new node to make an included angle gentle;
b spline curve:
basis functions:
cubic B-spline curve: and n is 3, and k is 0, 1, 2 and 3 for any four adjacent nodes, and the curve expression is as follows:
and (4) obtaining a control point set by using an optimized inverse solution control point algorithm for the curves, and adding and updating the planning path points. Therefore, by pruning and smoothing the path points, the calculation efficiency is improved, and the real-time performance is ensured.
The vehicle path planning method according to the embodiment of the present application is further described below with reference to the accompanying drawings.
As shown in fig. 2, fig. 2 is a schematic structural diagram of a vehicle path planning system related to the vehicle path planning method according to the embodiment of the present application. The vehicle path planning system includes: the device comprises a positioning module, a sensing module, a vehicle body module, a decision-making module and a control module. The positioning module can acquire map positioning information, the sensing module can acquire sensing data and environmental information of obstacles, and the vehicle body module can acquire vehicle information; the decision module can comprise a calculation unit and a storage device, all modeling and calculation in the decision module are completed in the calculation unit, and the generated path points and the updated path points are stored in the storage device; the decision module is divided into four sub-modules: environment modeling, (obstacle) behavior prediction, (obstacle) trajectory prediction, path planning. The control module is used for controlling according to the control instruction.
Specifically, as shown in fig. 3, the vehicle path planning method includes:
s301, sensing data and map positioning information of the obstacles are collected.
And S302, environment modeling.
And S303, behavior prediction.
The embodiment of the application can obtain the action intention of the obstacle at the next moment through the time series model
And S304, predicting the track.
According to the method and the device, the predicted track of the obstacle within the preset time can be obtained by adding the prediction model.
S305, generating a plurality of path points according to the vehicle information of the vehicle and the predicted path in the preset time length.
And S306, smoothing the plurality of path points, screening out the plurality of path points which do not meet the preset condition based on the maximum curvature constraint, connecting the plurality of remaining path points, and generating the vehicle driving path. According to the vehicle path planning method provided by the embodiment of the application, the motion information of the obstacles is mapped into the coordinate system of the global planning map through the vehicle body coordinate system, the action intention of the next moment is predicted according to the historical behavior and the current state of the obstacles, the action intention of each dynamic obstacle at the next moment is predicted step by step, a new environment model is formed and is sequentially increased in order to obtain the predicted track within the preset time length, so that the motion tracks of the obstacles are collected, reasonable local path points are planned, and the path points are processed in multiple steps to obtain the optimal path. Therefore, the state and constraint limitation of the vehicle are fully considered, the feasibility of the planned path is greatly improved based on environment modeling, the driving safety is improved, and the problem of path planning of dynamic obstacles or dynamic and static obstacles is solved.
Next, a vehicle path planning apparatus according to an embodiment of the present application will be described with reference to the drawings.
Fig. 4 is a block diagram of a vehicle path planning apparatus according to an embodiment of the present application.
As shown in fig. 4, the vehicle path planning apparatus 10 includes: an acquisition module 100, a prediction module 200, and a generation module 300.
The acquisition module 100 is configured to acquire sensing data of an obstacle and map positioning information;
the prediction module 200 is configured to predict a predicted trajectory of the obstacle within a preset time according to the sensing data and the map positioning information; and
the generating module 300 is configured to generate a plurality of path points according to the vehicle information of the vehicle and the predicted trajectory within the preset time duration to generate a vehicle driving path.
Optionally, after generating the plurality of waypoints, the generating module 300 is further configured to:
smoothing the multiple path points, and obtaining and screening the multiple path points which do not meet the preset conditions based on the maximum curvature constraint;
the remaining plurality of route points are connected to generate a vehicle travel route.
Optionally, the prediction module 200 is specifically configured to:
generating current time state data of the barrier according to the sensing data and the map positioning information;
and predicting to obtain the state data of the obstacle at the next moment according to the state data at the current moment to obtain the predicted track at the next moment so as to obtain the predicted track within the preset time length.
Optionally, the prediction module 200 is further configured to:
and obtaining the action intention of the next moment by utilizing a time series model according to the action states of the dynamic barrier in the environment at the previous moment and the current moment.
Optionally, the prediction module 200 is further configured to:
and gradually predicting the behavior intention of each dynamic obstacle at the next moment by using the incremental prediction model to form a new environment model, and sequentially increasing to obtain a predicted track within a preset time length.
Optionally, the acquisition module 100 is specifically configured to:
acquiring a pre-drawn global planning map, wherein the global planning map comprises a lane topological relation and geometric information;
acquiring the motion information of the obstacle, wherein the motion information comprises a geometric state, position information, speed information, acceleration information and/or course information;
and mapping the motion information into a coordinate system of the global planning map from the vehicle body coordinate system.
It should be noted that the foregoing explanation of the embodiment of the vehicle path planning method is also applicable to the vehicle path planning apparatus of the embodiment, and is not repeated herein.
According to the vehicle path planning device provided by the embodiment of the application, the motion information of the obstacles is mapped into the coordinate system of the global planning map through the vehicle body coordinate system, the action intention of the next moment is predicted according to the historical behavior and the current state of the obstacles, the action intention of each dynamic obstacle at the next moment is predicted step by step, a new environment model is formed and is sequentially increased in order to obtain the predicted track within the preset time length, so that the motion tracks of the obstacles are collected, reasonable local path points are planned, and the path points are subjected to multi-step processing to obtain the optimal path. Therefore, the state and constraint limitation of the vehicle are fully considered, the feasibility of the planned path is greatly improved based on environment modeling, the driving safety is improved, and the problem of path planning of dynamic obstacles or dynamic and static obstacles is solved.
Fig. 5 is a block diagram schematically illustrating a vehicle according to an embodiment of the present disclosure. The vehicle 20 includes the vehicle path planning apparatus 10 described above.
According to the vehicle provided by the embodiment of the application, the state and the constraint limit of the vehicle are fully considered through the vehicle path planning device, the feasibility of the planned path is greatly improved based on environmental modeling, and the driving safety is improved, so that the problem of path planning existing in both dynamic obstacles and dynamic and static obstacles is solved.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
a memory 1201, a processor 1202, and a computer program stored on the memory 1201 and executable on the processor 1202.
The processor 1202, when executing the program, implements the vehicle path planning method provided in the above-described embodiments.
Further, the electronic device further includes:
a communication interface 1203 for communication between the memory 1201 and the processor 1202.
A memory 1201 for storing computer programs executable on the processor 1202.
The memory 1201 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 1201, the processor 1202 and the communication interface 1203 are implemented independently, the communication interface 1203, the memory 1201 and the processor 1202 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 1201, the processor 1202, and the communication interface 1203 are integrated on a chip, the memory 1201, the processor 1202, and the communication interface 1203 may complete mutual communication through an internal interface.
The present embodiment also provides a computer-readable storage medium having a computer program stored thereon, wherein the program is configured to implement the vehicle path planning method as above when executed by a processor.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of implementing the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A vehicle path planning method, comprising the steps of:
acquiring sensing data and map positioning information of the barrier;
predicting to obtain a predicted track of the obstacle within a preset time length according to the perception data and the map positioning information; and
and generating a plurality of path points according to the vehicle information of the vehicle and the predicted path in the preset time length so as to generate a vehicle running path.
2. The method of claim 1, after generating the plurality of waypoints, further comprising:
smoothing the path points, and obtaining a plurality of path points which do not meet preset conditions by screening based on maximum curvature constraint;
and connecting the remaining route points to generate the vehicle driving route.
3. The method of claim 1, wherein the predicting the predicted trajectory of the obstacle within a preset time period according to the perception data and the mapping information comprises:
generating current time state data of the barrier according to the perception data and the map positioning information;
and predicting to obtain the state data of the barrier at the next moment according to the state data at the current moment to obtain the predicted track at the next moment so as to obtain the predicted track within the preset time length.
4. The method of claim 3, wherein generating the current-time status data of the obstacle from the perception data and the mapping information comprises:
and obtaining the action intention of the next moment by utilizing a time series model according to the action states of the dynamic barrier in the environment at the previous moment and the current moment.
5. The method according to claim 4, wherein the estimating, according to the current time state data, the obstacle next time state data to obtain a next time predicted trajectory to obtain the predicted trajectory within a preset time duration includes:
and gradually predicting the behavior intention of each dynamic obstacle at the next moment by using the incremental prediction model to form a new environment model, and sequentially increasing to obtain the predicted track within the preset time length.
6. The method of claim 1, wherein the collecting sensory data and mapping information of the obstacle comprises:
acquiring a pre-drawn global planning map, wherein the global planning map comprises a lane topological relation and geometric information;
acquiring the motion information of the obstacle, wherein the motion information comprises a geometric state, position information, speed information, acceleration information and/or course information;
and mapping the motion information into a coordinate system of the global planning map by a vehicle body coordinate system.
7. A vehicle path planning apparatus, comprising:
the acquisition module is used for acquiring sensing data of obstacles and map positioning information;
the prediction module is used for predicting to obtain a predicted track of the obstacle within a preset time length according to the perception data and the map positioning information; and
and the generating module is used for generating a plurality of path points according to the vehicle information of the vehicle and the predicted path in the preset time length so as to generate a vehicle running path.
8. A vehicle, characterized by comprising: the vehicle path planner according to claim 7.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the vehicle path planning method according to any one of claims 1-6.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing the vehicle path planning method according to any one of claims 1-6.
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