CN111998864A - Unmanned vehicle local path planning method, device, equipment and storage medium - Google Patents
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- G—PHYSICS
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Abstract
The invention relates to the technical field of unmanned driving, and discloses a method, a device, equipment and a storage medium for planning a local path of an unmanned vehicle, wherein the method comprises the following steps: the method comprises the steps of obtaining target environment information in a preset range of a target vehicle, determining an environment crowding degree grade according to the target environment information, generating discrete path points based on a preset global reference path and the environment crowding degree grade, generating a local path curve according to the discrete path points, and inputting the local path curve to a preset deep neural network model to obtain an optimal local path curve. According to the method and the device, the optimal local path curve is obtained by inputting the local path curve determined according to the preset global reference path and the environmental congestion degree level into the preset deep neural network model so as to achieve comprehensive evaluation of the target environmental information, and compared with the prior art that the local reference path is obtained through a single sensor and a vehicle wireless communication technology, the accuracy of unmanned vehicle local path planning is improved, and the safety of unmanned driving is also improved.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to a method, a device, equipment and a storage medium for planning a local path of an unmanned vehicle.
Background
With the development of the unmanned driving technology, the unmanned vehicle may become a mainstream travel mode of the public in the future, but due to the improvement of the living standard of the public, the vehicle cardinality of manual driving in a large city with more vehicles is huge. Accordingly, an unpredictable complex scene may occur in the process of advancing of the unmanned vehicle, so that traffic accidents are caused, how to improve the local path planning efficiency and accuracy of the unmanned vehicle and improve the safety of unmanned driving becomes a problem to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for planning a local path of an unmanned vehicle, and aims to solve the technical problems of improving the efficiency and the accuracy of planning the local path of the unmanned vehicle and improving the safety of unmanned driving.
In order to achieve the aim, the invention provides a method for planning the local path of an unmanned vehicle, which comprises the following steps:
when the target vehicle is detected to start the unmanned driving mode, acquiring target environment information within a preset range of the target vehicle, and determining the degree level of the environmental congestion according to the target environment information;
generating discrete path points based on a preset global reference path and the environment congestion degree grade, and generating a local path curve according to the discrete path points;
and inputting the local path curve into a preset deep neural network model to obtain an optimal local path curve, and controlling the target vehicle to run according to the optimal local path curve.
Preferably, the step of acquiring target environment information within a preset range of the target vehicle when it is detected that the target vehicle starts the unmanned driving mode specifically includes:
when the unmanned driving mode of a target vehicle is detected to be started, acquiring environmental information within a preset range of the target vehicle;
and performing coordinate system conversion processing on the environment information to obtain target environment information in a Frenet coordinate system.
Preferably, the step of determining the level of the environmental congestion degree according to the target environmental information specifically includes:
extracting obstacle information from the target environment information, and determining the number of obstacles according to the obstacle information;
and determining the environmental congestion degree level according to the number of the obstacles.
Preferably, before the step of generating discrete path points based on a preset global reference path and the environmental congestion degree level, and generating a local path curve according to the discrete path points, the method further includes:
extracting current coordinate information of the target vehicle from the target environment information, and acquiring destination coordinate information;
and generating a preset global reference path according to the current coordinate information and the destination coordinate information.
Preferably, the step of generating discrete path points based on a preset global reference path and the environmental congestion degree level, and generating a local path curve according to the discrete path points specifically includes:
intercepting a preset global reference path with a preset length in real time as a local reference path of the target vehicle, and performing segmentation processing on the local reference path to obtain the local reference path after the segmentation processing;
determining discrete path points according to the environment congestion degree grade and the local reference path after the segmentation processing;
and generating a discrete path point set based on the discrete path points, and fitting the discrete path point set to obtain a local path curve.
Preferably, before the step of inputting the local path curve into a preset deep neural network model to obtain an optimal local path curve and controlling the target vehicle to travel according to the optimal local path curve, the method further includes:
acquiring convolution kernel weight of a Bayes convolution neural network model, and training the convolution kernel weight through a preset obstacle evasion path curve training set to obtain a weight distribution result;
obtaining a loss function based on the weight distribution result, and judging whether the loss function meets a preset loss condition;
and when the loss function meets the preset loss condition, judging that the model training is finished, and taking the trained Bayes convolution neural network model as a preset deep neural network model.
Preferably, the step of inputting the local path curve into a preset deep neural network model to obtain an optimal local path curve and controlling the target vehicle to run according to the optimal local path curve includes:
inputting the local path curve to a preset deep neural network model to obtain a path evaluation parameter corresponding to the local path curve;
and determining an optimal local path curve according to the path evaluation parameters, and controlling the target vehicle to run according to the optimal local path curve.
In addition, in order to achieve the above object, the present invention further provides a device for planning a local path of an unmanned vehicle, the device comprising the following steps:
the congestion degree determining module is used for acquiring target environment information within a preset range of a target vehicle when the unmanned driving mode of the target vehicle is detected to be started, and determining the level of the environmental congestion degree according to the target environment information;
the path planning module is used for generating discrete path points based on a preset global reference path and the environment congestion degree grade and generating a local path curve according to the discrete path points;
and the path optimizing module is used for inputting the local path curve to a preset deep neural network model so as to obtain an optimal local path curve and controlling the target vehicle to run according to the optimal local path curve.
In addition, in order to achieve the above object, the present invention further provides an unmanned vehicle local path planning apparatus, including: a memory, a processor, and an unmanned vehicle local path planning program stored on the memory and executable on the processor, the unmanned vehicle local path planning program configured to implement the steps of the unmanned vehicle local path planning method as described above.
In addition, in order to achieve the above object, the present invention further provides a storage medium, where an unmanned vehicle local path planning program is stored, and the unmanned vehicle local path planning program, when executed by a processor, implements the steps of the unmanned vehicle local path planning method as described above.
When the unmanned driving mode of the target vehicle is detected, target environment information in a preset range of the target vehicle is obtained, the degree of environment congestion is determined according to the target environment information, discrete path points are generated based on a preset global reference path and the degree of environment congestion, a local path curve is generated according to the discrete path points, the local path curve is input to a preset deep neural network model to obtain an optimal local path curve, and the target vehicle is controlled to run according to the optimal local path curve. Compared with the prior art that a local reference path is obtained through a single sensor and an automotive wireless communication technology, the method and the device for obtaining the local reference path determine the level of the environmental congestion degree according to the target environment information, and then generate a local path curve based on the preset global reference path and the level of the environmental congestion degree, so that the computation amount is reduced, the computation efficiency is also improved, further, the local path planning efficiency of the unmanned vehicle is also improved, the optimal local path curve is obtained by inputting the local path curve to the preset deep neural network model, the comprehensive evaluation of the target environment information is realized, the local path planning accuracy of the unmanned vehicle is improved, the local path planning cost of the unmanned vehicle is reduced, and the safety of unmanned driving is also improved.
Drawings
Fig. 1 is a schematic structural diagram of an unmanned vehicle local path planning device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a first embodiment of a method for planning a local path of an unmanned vehicle according to the present invention;
FIG. 3 is a schematic flow chart illustrating a second embodiment of the unmanned vehicle local path planning method according to the present invention;
fig. 4 is a block diagram of the unmanned vehicle local path planning apparatus according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an unmanned vehicle local path planning device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the unmanned vehicle local path planning apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the unmanned vehicle local path planning apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and an unmanned vehicle partial path planning program.
In the unmanned vehicle local path planning apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the unmanned vehicle local path planning device of the present invention may be disposed in the unmanned vehicle local path planning device, and the unmanned vehicle local path planning device calls the unmanned vehicle local path planning program stored in the memory 1005 through the processor 1001, and executes the unmanned vehicle local path planning method provided by the embodiment of the present invention.
The embodiment of the invention provides a method for planning a local path of an unmanned vehicle, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the method for planning the local path of the unmanned vehicle.
In this embodiment, the unmanned vehicle local path planning method includes the following steps:
step S10: when the target vehicle is detected to start the unmanned driving mode, acquiring target environment information within a preset range of the target vehicle, and determining the degree level of the environmental congestion according to the target environment information;
it is easy to understand that the execution subject of the embodiment is the above unmanned vehicle local path planning device, which has functions of data processing, data communication, program operation, and the like, and may be mounted on the target vehicle, or may be a device built in the target vehicle, which is not limited in this embodiment. When detecting that the unmanned driving mode of the target vehicle is started, the unmanned vehicle local path planning device may acquire, by using a preset camera device and a millimeter wave radar, environment information of the target vehicle within a preset range, where the environment information may include obstacle information and current coordinate information of the target vehicle, the current coordinate information may be geographic coordinates of the target vehicle corresponding to a World coordinate system (World coordinate system) and image information of the target vehicle itself, the obstacle information may be image information of obstacles obstructing a driving path of the target vehicle, such as pedestrians, trees, other vehicles except the target vehicle, and the like, and geographic coordinates corresponding to the World coordinate system, the preset range may be within a circle with the current geographic coordinates of the target vehicle as a center of the circle and a first preset distance as a radius, or within a second preset distance in front of the target vehicle, if the front angle of the target vehicle is 30 ° and the radius is within the sector range of 100 meters, the first preset distance and the second preset distance are not limited in this embodiment, and may be set according to actual requirements.
In a specific implementation, in order to improve the generation efficiency of the local path curve, coordinate system conversion processing may be performed on the environment information to obtain target environment information in a Frenet coordinate system (also called Frenet-Serret equation). In a specific implementation, the geographic coordinates (X) of the target vehicle in the world coordinate system can be extracted from the environment informationW，YW，ZW) Wherein, an X axis is established based on the driving direction of the target vehicle (which can be the advancing direction or the head direction of the target vehicle), a Y axis is established based on the direction parallel to the road surface and vertical to the X axis, and a Z axis is established based on the direction vertical to the plane formed by the X axis and the Y axis. Then with (X)W，YW，ZW) Coordinate system conversion is performed for the origin to obtain a new origin (X) in the Camera coordinate system (Camera coordinate system)C，YC，ZC) Then, with (X)C，YC，ZC) The method comprises the steps of performing coordinate system conversion processing on an origin to obtain a new origin (X, Y) under an image coordinate system (Pixel coordinate system), and then performing coordinate system conversion processing on the origin (X, Y) to obtain target environment information under a Frenet coordinate system, wherein the Frenet coordinate system enables the target environment information to be more easily identified by a preset deep neural network model described below, the motion relation among different objects on a road can be more intuitively represented, and the generation efficiency of a local path curve is further improved.
It is easy to understand that after the target environment information is obtained, obstacle information may be extracted from the target environment information, and the number of obstacles is determined according to the obstacle information, and then an environment congestion degree level is determined according to the number of obstacles, where the environment congestion degree level may be divided into three levels, i.e. high, medium and low, and may be understood that different numbers of obstacles correspond to different environment congestion degree levels, for example, when the number of obstacles is greater than or equal to 4, the corresponding environment congestion degree level is high, when the number of obstacles is greater than 0 and less than 4, the corresponding environment congestion degree level is medium, when no obstacles exist, the corresponding environment congestion degree level is low, and the corresponding relationship between the number of obstacles and the environment congestion degree level may be obtained according to the corresponding relationship between the number of obstacles and the environment congestion degree level in a preset congestion degree relationship mapping table, the adjustment can also be made according to the historical correspondence, which is not limited by the present embodiment.
It should be noted that after the target environment information is obtained, the current coordinate information of the target vehicle can be extracted from the target environment information, and the destination coordinate information can be obtained, then generating a preset global reference path according to the current coordinate information and the destination coordinate information, the destination coordinate information may be coordinate information of a destination (including location coordinates of the destination and image information of the destination) input by a user, the preset global reference path may be a reference path planned based on location coordinates of the target vehicle in the current coordinate information and geographical coordinates of the destination in the destination coordinate information to facilitate a preview of the global, after the preset global reference path is obtained, further refining is carried out on the basis of the preset global reference path to obtain a local reference path, and then a local path curve is generated on the basis of the local reference path to improve the accuracy of local path planning.
Step S20: generating discrete path points based on a preset global reference path and the environment congestion degree grade, and generating a local path curve according to the discrete path points;
it should be noted that, after obtaining the preset global reference path and the environmental congestion degree level, a preset global reference path with a preset length (for example, 100 meters) may be intercepted in real time as a local reference path of the target vehicle, and the local reference path is segmented to obtain the segmented local reference path (which may be divided into equal segments, for example, 20 meters per segment, if the local reference path is divided into 5 segments), and then discrete path points are determined according to the environmental congestion degree level and the segmented local reference path, which may be understood that different environmental congestion degree levels correspond to different discrete path points, for example, when the environmental congestion degree level is high, the number of corresponding discrete path points is 5 (i.e., 5 discrete path points are set in a tangential direction of the segment points of the 20 meter long segmented local reference path), the number of equally divided discrete points is 3 (i.e., 3 discrete route points are set in the tangential direction of the segment points of the local reference route after the segmentation processing of 20 meters long), when the environmental congestion degree level is low, the number of equally divided discrete points is 1 (i.e., 1 discrete route point is set in the tangential direction of the segment points of the local reference route after the segmentation processing of 20 meters long), the preset length may be determined according to actual requirements, the correspondence between the environmental congestion degree level and the discrete route points may be obtained according to the correspondence between the environmental congestion degree level and the discrete route points in the preset discrete point relationship mapping table, or may be adjusted according to historical correspondence, which is not limited in this embodiment.
It is easy to understand that after the discrete path points are obtained, one discrete path point can be selected from different discrete path points corresponding to the tangent direction of the segment point of the local reference path after the segmentation processing in real time to form a discrete path point set, and if the discrete path point set is divided into 5 segments, the discrete path point set can be { P {1i，P2i，P3i，P4i，P5i}，P1i、P2i、P3i、P4i、P5iThe discrete path points in the tangential direction of the five segment points are respectively, i represents the position sequence of the discrete path points in the direction where the discrete path points are located, and then a unitary cubic equation is used to fit the discrete path point set to obtain a local path curve in the Frenet coordinate system, where the unitary cubic equation may be determined according to actual requirements, which is not limited in this embodiment.
Step S30: and inputting the local path curve into a preset deep neural network model to obtain an optimal local path curve, and controlling the target vehicle to run according to the optimal local path curve.
It is easy to understand that, after the local path curve is obtained, the local path curve may be input to a preset deep neural network model, a path evaluation parameter corresponding to the local path curve is obtained, the path evaluation parameter may be a rating or a total rating made by the preset deep neural network model on a slope, a curvature, a distance, time corresponding to the local path curve, and the number of traffic lights, driving stability, and the like on a road corresponding to the local path curve, and then an optimal local path curve is determined according to the path evaluation parameter.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
In this embodiment, when it is detected that an unmanned driving mode of a target vehicle is started, target environment information within a preset range of the target vehicle is acquired, an environment congestion degree grade is determined according to the target environment information, discrete path points are generated based on a preset global reference path and the environment congestion degree grade, a local path curve is generated according to the discrete path points, the local path curve is input to a preset deep neural network model to obtain an optimal local path curve, and the target vehicle is controlled to run according to the optimal local path curve. Compared with the prior art that a local reference path is obtained through a single sensor and an automotive wireless communication technology, the method and the device for obtaining the local reference path determine the level of the environmental congestion degree according to the target environment information, and then generate a local path curve based on the preset global reference path and the level of the environmental congestion degree, so that the computation amount is reduced, the computation efficiency is also improved, further, the local path planning efficiency of the unmanned vehicle is also improved, the optimal local path curve is obtained by inputting the local path curve to the preset deep neural network model, the comprehensive evaluation of the target environment information is realized, the local path planning accuracy of the unmanned vehicle is improved, the local path planning cost of the unmanned vehicle is reduced, and the safety of unmanned driving is also improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of a third embodiment of the unmanned vehicle local path planning method according to the present invention.
Based on the foregoing embodiments, in this embodiment, before the step S30, the method further includes:
step S001: acquiring convolution kernel weight of a Bayes convolution neural network model, and training the convolution kernel weight through a preset obstacle evasion path curve training set to obtain a weight distribution result;
step S002: obtaining a loss function based on the weight distribution result, and judging whether the loss function meets a preset loss condition;
step S003: and when the loss function meets the preset loss condition, judging that the model training is finished, and taking the trained Bayes convolution neural network model as a preset deep neural network model.
It should be noted that, the bayesian convolutional neural network model is established based on a bayesian convolutional neural network, and is adaptively defined based on the unmanned vehicle local path planning method described in the present application, in the bayesian convolutional neural network model, a convolutional layer, an active layer, and a pooling layer are in a serial relationship, that is, the convolutional layer → the active layer → the pooling layer → the loss function (loss function), only the convolutional kernel weight of the bayesian convolutional neural network model needs to be obtained, and then the convolutional kernel weight is trained through a preset obstacle avoidance path curve training set (which can be understood as a training set for performing local path planning training on the bayesian convolutional neural network model), so as to obtain a weight distribution result (which can be understood as a set of a priori distribution and posterior distribution corresponding to the convolutional kernel weight), and based on the weight distribution result, performing variable Bayesian estimation (variable Bayesian estimation) on the weight distribution result according to an optimal control theory to obtain a loss function, wherein the variable Bayesian estimation can perform local optimal estimation on posterior distribution of hidden variables (latent variables) of a probability model (namely the Bayesian convolutional neural network model) in a given variable family (namely the weight distribution result) in an iterative manner, then judging whether the loss function meets a preset loss condition, judging that the model training is finished when the loss function meets the preset loss condition, and taking the trained Bayesian convolutional neural network model as a preset depth neural network model. In the specific implementation, after the Loss function Loss is obtained, whether the model training is completed or not can be judged through the following formula, that is, when the Loss function Loss approaches to 0 or is 0, the model training can be judged to be completed, and the trained Bayes convolution neural network model is used as the preset deep neural network model.
Loss＝EQ[logQ(wi|α)-logP(wi)]-EQ(logP(S|wi)]
In the formula, wiRepresents the ith convolution kernel weight, EQThe expression is expected value, Q is variation posterior distribution, P is variation prior distribution, alpha is preset parameter according with Gaussian distribution, and S is preset obstacle evasion path curve training set.
In the embodiment, a convolution kernel weight of a Bayes convolution neural network model is obtained, the convolution kernel weight is trained through a preset obstacle evasion path curve training set to obtain a weight distribution result, a loss function is obtained based on the weight distribution result, whether the loss function meets a preset loss condition or not is judged, when the loss function meets the preset loss condition, the completion of model training is judged, and the trained Bayes convolution neural network model is used as a preset deep neural network model. And training the convolution kernel weight of the Bayes convolution neural network model to obtain a weight distribution result. Different from the prior art that the optimal local path curve is obtained by only performing superposition processing on the obtained road condition characteristics when local path planning is performed, the preset deep neural network model required by the application is obtained based on the Bayes convolution neural network model, and then local path planning is performed through the preset deep neural network model so as to improve the model precision and the model depth of the preset deep neural network model, so that the planning precision when local path planning is performed based on the preset deep neural network model is also improved, and further, the safety of unmanned driving is also improved.
In addition, an embodiment of the present invention further provides a storage medium, where the storage medium stores an unmanned vehicle local path planning program, and the unmanned vehicle local path planning program, when executed by a processor, implements the steps of the unmanned vehicle local path planning method described above.
Referring to fig. 4, fig. 4 is a block diagram of a first embodiment of the unmanned vehicle local path planning apparatus according to the present invention.
As shown in fig. 4, the apparatus for planning a local path of an unmanned vehicle according to an embodiment of the present invention includes:
the congestion degree determining module 10 is configured to, when it is detected that the unmanned driving mode of the target vehicle is started, acquire target environment information within a preset range of the target vehicle, and determine an environment congestion degree level according to the target environment information;
the path planning module 20 is configured to generate discrete path points based on a preset global reference path and the environmental congestion degree level, and generate a local path curve according to the discrete path points;
and the path optimizing module 30 is configured to input the local path curve to a preset deep neural network model to obtain an optimal local path curve, and control the target vehicle to travel according to the optimal local path curve.
When the unmanned driving mode of the target vehicle is detected, target environment information in a preset range of the target vehicle is obtained, the degree of environment congestion is determined according to the target environment information, discrete path points are generated based on a preset global reference path and the degree of environment congestion, a local path curve is generated according to the discrete path points, the local path curve is input to a preset deep neural network model to obtain an optimal local path curve, and the target vehicle is controlled to run according to the optimal local path curve. Compared with the prior art that a local reference path is obtained through a single sensor and an automotive wireless communication technology, the method and the device for obtaining the local reference path determine the level of the environmental congestion degree according to the target environment information, and then generate a local path curve based on the preset global reference path and the level of the environmental congestion degree, so that the computation amount is reduced, the computation efficiency is also improved, further, the local path planning efficiency of the unmanned vehicle is also improved, the optimal local path curve is obtained by inputting the local path curve to the preset deep neural network model, the comprehensive evaluation of the target environment information is realized, the local path planning accuracy of the unmanned vehicle is improved, the local path planning cost of the unmanned vehicle is reduced, and the safety of unmanned driving is also improved.
Based on the first embodiment of the unmanned vehicle local path planning device, a second embodiment of the unmanned vehicle local path planning device is provided.
In this embodiment, the congestion degree determining module 10 is further configured to, when it is detected that the target vehicle starts the unmanned driving mode, obtain environmental information within a preset range of the target vehicle;
the congestion degree determining module 10 is further configured to perform coordinate system conversion processing on the environment information to obtain target environment information in a Frenet coordinate system.
The congestion degree determining module 10 is further configured to, when it is detected that the target vehicle starts the unmanned driving mode, obtain target environment information within a preset range of the target vehicle;
the congestion degree determining module 10 is further configured to extract obstacle information from the target environment information, and determine the number of obstacles according to the obstacle information;
the congestion degree determining module 10 is further configured to determine an environmental congestion degree level according to the number of obstacles.
The path planning module 20 is further configured to extract current coordinate information of the target vehicle from the target environment information, and obtain destination coordinate information;
the path planning module 20 is further configured to generate a preset global reference path according to the current coordinate information and the destination coordinate information.
The path planning module 20 is further configured to intercept a preset global reference path with a preset length in real time as a local reference path of the target vehicle, and perform segmentation processing on the local reference path to obtain the local reference path after the segmentation processing;
the path planning module 20 is further configured to determine discrete path points according to the environmental congestion degree level and the local reference path after the segmentation processing;
the path planning module 20 is further configured to generate a discrete path point set based on the discrete path points, and fit the discrete path point set to obtain a local path curve.
The path optimizing module 30 is further configured to obtain a convolution kernel weight of the bayesian convolutional neural network model, and train the convolution kernel weight through a preset obstacle avoidance path curve training set to obtain a weight distribution result;
the path optimizing module 30 is further configured to obtain a loss function based on the weight distribution result, and determine whether the loss function meets a preset loss condition;
the path optimizing module 30 is further configured to determine that the model training is completed when the loss function meets the preset loss condition, and use the trained bayesian convolutional neural network model as a preset deep neural network model.
The path optimizing module 30 is further configured to input the local path curve to a preset deep neural network model, and obtain a path evaluation parameter corresponding to the local path curve;
the path optimizing module 30 is further configured to determine an optimal local path curve according to the path evaluation parameter, and control the target vehicle to travel according to the optimal local path curve.
Other embodiments or specific implementation manners of the unmanned vehicle local path planning device of the invention can refer to the above method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for planning a local path of an unmanned vehicle is characterized by comprising the following steps:
when the target vehicle is detected to start the unmanned driving mode, acquiring target environment information within a preset range of the target vehicle, and determining the degree level of the environmental congestion according to the target environment information;
generating discrete path points based on a preset global reference path and the environment congestion degree grade, and generating a local path curve according to the discrete path points;
and inputting the local path curve into a preset deep neural network model to obtain an optimal local path curve, and controlling the target vehicle to run according to the optimal local path curve.
2. The method according to claim 1, wherein the step of acquiring the target environment information within the preset range of the target vehicle when detecting that the unmanned driving mode of the target vehicle is started comprises:
when the unmanned driving mode of a target vehicle is detected to be started, acquiring environmental information within a preset range of the target vehicle;
and performing coordinate system conversion processing on the environment information to obtain target environment information in a Frenet coordinate system.
3. The method according to claim 1, wherein the step of determining the level of the environmental congestion degree based on the target environmental information specifically includes:
extracting obstacle information from the target environment information, and determining the number of obstacles according to the obstacle information;
and determining the environmental congestion degree level according to the number of the obstacles.
4. The method of claim 1, wherein before the step of generating discrete waypoints based on the preset global reference path and the level of environmental congestion and generating a local path curve from the discrete waypoints, the method further comprises:
extracting current coordinate information of the target vehicle from the target environment information, and acquiring destination coordinate information;
and generating a preset global reference path according to the current coordinate information and the destination coordinate information.
5. The method according to claim 1, wherein the step of generating discrete path points based on a preset global reference path and the environmental congestion level and generating a local path curve according to the discrete path points specifically includes:
intercepting a preset global reference path with a preset length in real time as a local reference path of the target vehicle, and performing segmentation processing on the local reference path to obtain the local reference path after the segmentation processing;
determining discrete path points according to the environment congestion degree grade and the local reference path after the segmentation processing;
and generating a discrete path point set based on the discrete path points, and fitting the discrete path point set to obtain a local path curve.
6. The method of claim 1, wherein before the step of inputting the local path profile into a preset deep neural network model to obtain an optimal local path profile and controlling the travel of the target vehicle according to the optimal local path profile, further comprising:
acquiring convolution kernel weight of a Bayes convolution neural network model, and training the convolution kernel weight through a preset obstacle evasion path curve training set to obtain a weight distribution result;
obtaining a loss function based on the weight distribution result, and judging whether the loss function meets a preset loss condition;
and when the loss function meets the preset loss condition, judging that the model training is finished, and taking the trained Bayes convolution neural network model as a preset deep neural network model.
7. The method according to claim 1, wherein the step of inputting the local path curve into a preset deep neural network model to obtain an optimal local path curve and controlling the target vehicle to travel according to the optimal local path curve comprises:
inputting the local path curve to a preset deep neural network model to obtain a path evaluation parameter corresponding to the local path curve;
and determining an optimal local path curve according to the path evaluation parameters, and controlling the target vehicle to run according to the optimal local path curve.
8. An unmanned vehicle local path planning device is characterized by comprising the following steps:
the congestion degree determining module is used for acquiring target environment information within a preset range of a target vehicle when the unmanned driving mode of the target vehicle is detected to be started, and determining the level of the environmental congestion degree according to the target environment information;
the path planning module is used for generating discrete path points based on a preset global reference path and the environment congestion degree grade and generating a local path curve according to the discrete path points;
and the path optimizing module is used for inputting the local path curve to a preset deep neural network model so as to obtain an optimal local path curve and controlling the target vehicle to run according to the optimal local path curve.
9. An unmanned vehicle local path planning apparatus, the apparatus comprising: a memory, a processor, and an unmanned vehicle local path planning program stored on the memory and executable on the processor, the unmanned vehicle local path planning program configured to implement the steps of the unmanned vehicle local path planning method of any of claims 1-7.
10. A storage medium having stored thereon an unmanned vehicle local path planning program which, when executed by a processor, implements the steps of the unmanned vehicle local path planning method of any one of claims 1 to 7.
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