CN112017426B - Training method of vehicle path transformation model, path transformation method and device - Google Patents
Training method of vehicle path transformation model, path transformation method and device Download PDFInfo
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Abstract
The embodiment of the invention discloses a training method of a vehicle path transformation model, a path transformation method and a device, wherein the method comprises the following steps: acquiring historical motion information of a current vehicle when the current vehicle receives a path change instruction, and historical motion information of other vehicles in corresponding historical environment perception information; generating a planning path which meets vehicle position constraint and enables a preset target function to reach a preset convergence condition according to historical motion information of the own vehicle and other vehicles; detecting a path transformation effect of the planned path, and taking a target driving path and corresponding historical motion information which meet the preset path transformation requirement in a detection result as a training sample set; and training the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement. By adopting the technical scheme, the accuracy and efficiency of path planning are improved.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a training method of a vehicle path transformation model, a path transformation method and a device.
Background
And (4) planning the motion, namely finding a path which meets the constraint condition for the unmanned vehicle between the given position A and the given position B. This constraint may be collision-free, shortest path, minimal mechanical work, etc. Is an important research field of robotics.
For safe and efficient unmanned vehicle planning, an optimal strategy can be provided for the unmanned vehicle by a planning method based on optimization solution. Most of the existing path planning algorithms are based on searching, a series of feasible paths meeting dynamics are generated, and then a more appropriate motion path is screened out through collision detection and some artificial path characteristics.
The above method has a high computational complexity in the implementation process, and cannot enumerate all feasible trajectories, and is not suitable for a real-time application process.
Disclosure of Invention
The embodiment of the invention discloses a training method of a vehicle path transformation model, a path transformation method and a device, which improve the accuracy and efficiency of path planning.
In a first aspect, an embodiment of the present invention discloses a training method for a vehicle path transformation model, including:
acquiring historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
generating a planned path which meets vehicle position constraint and enables a preset target function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the target function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle;
detecting a path transformation effect of the planned path, and taking a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
and training the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement.
Optionally, the planned path is generated in an iterative manner, and a kinematic parameter value meeting the vehicle position constraint, obtained each time according to the historical motion information of the own vehicle and the historical motion information of the other vehicles, is used as an input of the next iteration until the planned path with the minimum preset objective function is generated;
wherein the vehicle position constraint comprises:
when the current vehicle is in the current lane, the longitudinal position of the current vehicle is smaller than that of the front vehicle in the running direction;
when the current vehicle runs to a target lane after lane change is performed, the longitudinal position of the current vehicle is greater than the longitudinal position of the vehicle behind the current vehicle in the running direction and is less than the longitudinal position of the vehicle in front of the current vehicle;
the objective function is
Wherein, c1Is a coefficient, a is acceleration, y is lateral position of the vehicle, ygoalThe lateral position of the vehicle in the target lane.
Optionally, a planned path that minimizes the preset objective function is generated according to the following iterative formula:
wherein the content of the first and second substances,
In a second aspect, an embodiment of the present invention further provides a training apparatus for a vehicle path transformation model, where the apparatus includes:
the historical motion information acquisition module is configured to acquire historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
the planned path generating module is configured to generate a planned path which meets vehicle position constraint and enables a preset objective function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the objective function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle;
the training sample set generation module is configured to detect a path transformation effect of the planned path, and take a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
and the path transformation model establishing module is configured to train the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, and the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement.
Optionally, the planned path is generated in an iterative manner, and a kinematic parameter value meeting the vehicle position constraint, obtained each time according to the historical motion information of the own vehicle and the historical motion information of the other vehicles, is used as an input of the next iteration until the planned path with the minimum preset objective function is generated;
wherein the vehicle position constraint comprises:
when the current vehicle is in the current lane, the longitudinal position of the current vehicle is smaller than that of the front vehicle in the running direction;
when the current vehicle runs to a target lane after lane change is performed, the longitudinal position of the current vehicle is greater than the longitudinal position of the vehicle behind the current vehicle in the running direction and is less than the longitudinal position of the vehicle in front of the current vehicle;
the objective function is
Wherein, c1Is a coefficient, a is acceleration, y is lateral position of the vehicle, ygoalThe lateral position of the vehicle in the target lane.
Optionally, a planned path that minimizes the preset objective function is generated according to the following iterative formula:
wherein the content of the first and second substances,
In a third aspect, an embodiment of the present invention further discloses a method for changing a path of a vehicle, where the method includes:
receiving environment perception information and a path transformation instruction, wherein the environment perception information comprises current motion information of other vehicles except a current vehicle, and the current motion information comprises; speed, position and acceleration;
according to the current motion information of the current vehicle and the current motion information of other vehicles, if a target driving path which is generated based on a path transformation model, meets vehicle position constraint and enables a preset target function to reach a preset convergence condition is obtained, path transformation operation is executed according to the target driving path;
wherein the objective function establishes an integral relationship between the acceleration, lateral position and time of the current vehicle; the path transformation model associates current motion information of a current vehicle and other vehicles with a target travel path of the current vehicle when the current vehicle is subjected to path change.
Optionally, the method further includes:
and according to the current motion information of the current vehicle and the current motion information of the other vehicles, if the target driving path is not acquired, keeping the driving state of the current vehicle in the current lane.
Optionally, before performing the path transformation operation according to the target travel path, the method further includes:
according to the kinematic parameter values corresponding to the discrete points of the target driving path, performing collision detection on the current vehicle and the other vehicles;
correspondingly, if the detection result shows that the current vehicle collides with other vehicles, the running state of the current vehicle in the current lane is kept;
and if the detection result shows that the current vehicle does not collide with other vehicles, executing the lane change action according to the target driving path.
Optionally, the path transformation model is constructed by:
acquiring historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
generating a planned path which meets vehicle position constraint and enables a preset target function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the target function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle;
detecting a path transformation effect of the planned path, and taking a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
and training the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement.
In a fourth aspect, an embodiment of the present invention further discloses a path changing device for a vehicle, including:
the system comprises a path transformation instruction acquisition module, a path transformation instruction acquisition module and a path transformation instruction processing module, wherein the path transformation instruction acquisition module is configured to receive environment perception information and a path transformation instruction, the environment perception information comprises current motion information of other vehicles except a current vehicle, and the current motion information comprises; speed, position and acceleration;
a target driving path generating module configured to execute a path transformation operation according to a target driving path which is generated based on a path transformation model and satisfies a vehicle position constraint and enables a preset target function to reach a preset convergence condition if the target driving path is acquired according to the current motion information of the current vehicle and the current motion information of the other vehicle;
wherein the objective function establishes an integral relationship between the acceleration, lateral position and time of the current vehicle; the path transformation model associates current motion information of a current vehicle and other vehicles with a target travel path of the current vehicle when the current vehicle is subjected to path change.
Optionally, the apparatus further comprises:
and the running state keeping module is configured to keep the running state of the current vehicle in the current lane if the target running path is not acquired according to the current motion information of the current vehicle and the current motion information of the other vehicles.
Optionally, the apparatus further comprises:
the collision detection module is configured to perform collision detection on the current vehicle and the other vehicle according to the kinematic parameter values corresponding to the discrete points of the target driving path;
correspondingly, if the detection result shows that the current vehicle collides with other vehicles, the running state of the current vehicle in the current lane is kept;
and if the detection result shows that the current vehicle does not collide with other vehicles, executing the lane change action according to the target driving path.
Optionally, the path transformation model is constructed by:
acquiring historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
generating a planned path which meets vehicle position constraint and enables a preset target function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the target function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle;
detecting a path transformation effect of the planned path, and taking a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
and training the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement.
In a fifth aspect, an embodiment of the present invention further provides a vehicle-mounted terminal, including:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program codes stored in the memory to execute part or all of the steps of the training method of the vehicle path transformation model provided by any embodiment of the invention.
In a sixth aspect, the present invention further provides a vehicle-mounted terminal, including:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to perform part or all of the steps of the method for path conversion of a vehicle provided by any embodiment of the present invention.
In a seventh aspect, the embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program, where the computer program includes instructions for executing part or all of the steps of the training method for a vehicle path transformation model provided in any embodiment of the present invention.
In an eighth aspect, the embodiment of the present invention further provides a computer-readable storage medium storing a computer program including instructions for executing part or all of the steps of the path transformation method for a vehicle provided in any embodiment of the present invention.
In a ninth aspect, embodiments of the present invention further provide a computer program product, which when run on a computer, causes the computer to perform part or all of the steps of the training method for a vehicle path transformation model provided in any of the embodiments of the present invention.
In a tenth aspect, embodiments of the present invention further provide a computer program product, which when run on a computer, causes the computer to execute part or all of the steps of the method for path transformation of a vehicle provided in any of the embodiments of the present invention.
According to the technical scheme provided by the embodiment of the invention, the planned path which meets the position constraint of the vehicle and enables the preset target function to reach the preset convergence condition can be generated by utilizing the historical motion information of the current vehicle and other vehicles, including speed, position, acceleration and the like. The objective function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle, and the current vehicle can accurately, quickly and stably carry out path change when receiving a path change instruction by enabling the objective function to be minimum. In addition, the generated planning path is subjected to path transformation effect detection, so that a target driving path meeting the requirement of preset path transformation can be screened out, and the accuracy of path transformation is further improved. The target driving path and the corresponding historical motion information are used as a training sample set, the initial neural network model is trained by using the training sample set, a path transformation model of the vehicle can be obtained, the target driving path when the path is changed with the current vehicle can be obtained by using the path transformation model according to the motion information of the current vehicle and other vehicles, and compared with a mode that a search algorithm is adopted to screen out an optimal path from a plurality of candidate paths in the prior art, the method improves the accuracy and the rapidity of vehicle path transformation.
The invention comprises the following steps:
1. by designing the target function and training the neural network model, the neural network model can output the target driving path which enables the target function to be minimum and meets the path transformation requirement according to the motion information of the own vehicle and other vehicles, the problem of high calculation complexity when the optimal path is screened out from a plurality of candidate paths through a search algorithm is solved, and the accuracy and the speed of planning the target driving path are improved.
2. According to the technical scheme provided by the embodiment of the invention, after the planned path is generated in an iterative manner, the screened target driving path is enabled to better accord with the driving standard of the vehicle by detecting the path transformation effect of the planned path, the problem of poor accuracy of the planned path obtained by using a search algorithm in the prior art is solved, and the rationality of path planning is further improved.
3. In the technical scheme of the embodiment of the invention, the initial neural network model is trained by taking the target driving path meeting the preset path transformation requirement and the corresponding historical motion information as a training sample set, so that the neural network model associates the motion information of the current vehicle and other vehicles with the target driving path when the current vehicle is subjected to path replacement. In the actual running process of the vehicle, the problem that the path planning speed is influenced by continuously executing the iterative algorithm is avoided, and the generation speed of the target running path is accelerated.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a training method for a vehicle path transformation model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a lane change provided by an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for changing a path of a vehicle according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a training apparatus for a vehicle path transformation model according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a path conversion device of a vehicle according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a vehicle-mounted terminal according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the embodiments and drawings of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart of a training method for a vehicle path transformation model according to an embodiment of the present invention. The method is applied to automatic driving, can be executed by a training device of a vehicle path transformation model, can be realized in a software and/or hardware mode, and can be generally integrated in vehicle-mounted terminals such as a vehicle-mounted Computer, a vehicle-mounted Industrial control Computer (IPC) and the like, and the embodiment of the invention is not limited. As shown in fig. 1, the method provided in this embodiment specifically includes:
110. and acquiring historical motion information of the current vehicle when the current vehicle receives the path change instruction and historical motion information of other vehicles except the current vehicle in the corresponding historical environment perception information.
The path change can be lane change, overtaking or parking on the right side of the road, etc. The path change instruction may be an instruction sent by the driver to the driving assistance system according to the actual running condition of the current vehicle, or may be a path change instruction automatically triggered by the current automatic driving vehicle according to the navigation information, or may also be a path change instruction triggered by the current automatic driving vehicle when detecting that the running track of another vehicle will affect the running track of the current vehicle.
The historical motion information comprises speed, position, acceleration and the like, and the motion information of the self vehicle and other vehicles can be acquired through sensors such as a camera or a radar.
120. And generating a planning path which meets the vehicle position constraint and enables a preset target function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of other vehicles.
In the process of changing the driving path of the vehicle, factors such as the safe distance between the vehicle and other vehicles, the time for changing the path, the stability and the like need to be considered. In this embodiment, by designing the objective function and minimizing the objective function, the vehicle can reach the target position in the fastest and most stable state, that is, the optimal planned path is obtained. Wherein the objective function establishes an integral relationship between the acceleration, the lateral position and the time of the current vehicle, which can be represented by the following formula:
wherein, c1Is a coefficient, a is acceleration, y is lateral position of the vehicle, ygoalThe lateral position of the vehicle in the target lane.
For example, the planned path may be generated in an iterative manner, and the kinematic parameter values meeting the vehicle position constraint, obtained each time according to the historical motion information of the own vehicle and the historical motion information of the other vehicles, are used as the input of the next iteration until the planned path that minimizes the preset objective function is generated. The kinematic parameters include lateral position, longitudinal position, speed, acceleration, and turning angle of the vehicle, among others.
Specifically, taking a vehicle lane change as an example, fig. 2 is a schematic diagram of a lane change provided by an embodiment of the present invention, as shown in fig. 2, 1 denotes a current vehicle, and 2, 3, and 4 denote other vehicles except the current vehicle. CL represents a current driving lane of the current vehicle, and TL represents a target lane of the current vehicle after lane change. As shown in fig. 2, when the present vehicle is in the present lane CL, the longitudinal position of the present vehicle 1 is smaller than the longitudinal position of the preceding vehicle 2 in the running direction; when the current vehicle travels to the target lane TL after performing the lane change, the longitudinal position of the current vehicle 1 is greater than the longitudinal position of the rear vehicle 3 behind the current vehicle in the traveling direction and is less than the longitudinal position of the front vehicle 4 in front of the current vehicle, which can be embodied by the following formula:
wherein the content of the first and second substances,
indicating the longitudinal position of the current vehicle,
represents the longitudinal position of the preceding vehicle 2 in the running direction when the current vehicle is in the current lane;
represents the longitudinal position of the vehicle 3 behind the current vehicle in the running direction when the current vehicle travels to the target lane after performing lane change;
indicating the longitudinal position of the preceding vehicle 4 ahead of the current vehicle in the running direction when the current vehicle travels to the target lane after performing the lane change.
Specifically, a linear system for representing vehicle motion information can be obtained according to the previous relation of each kinematic parameter
After discretization, generating a planning path which enables a preset objective function to be minimum according to the following iterative formula:
wherein the content of the first and second substances,
In this embodiment, a planned path that minimizes the preset objective function may be generated by using the above iteration method, the planned path is discretized according to time points, and each discrete point has a kinematic parameter value corresponding to a time. The method for directly obtaining the planned path according to the iterative formula provided by the embodiment reduces the complexity of screening the optimal path from a plurality of candidate planned paths according to a search algorithm in the prior art, and the accuracy of path planning can be improved by the arrangement of the embodiment.
130. And detecting the path transformation effect of the planned path, and taking the target driving path meeting the preset path transformation requirement in the detection result and corresponding historical motion information as a training sample set.
In this embodiment, for the generated planned path that satisfies the vehicle position constraint and makes the preset objective function reach the preset convergence condition, the planned path may be a path that does not meet the preset path change requirement, for example, the vehicle crosses the middle of a lane line, or the vehicle tends to return to its own lane during the lane change process. Therefore, for the planned route, it is possible to determine whether the vehicle body is always in the line-pressing state, or whether the change in the lateral position of the vehicle body exceeds a set threshold, or the like, within a set period of time while the vehicle is traveling along the planned route. The embodiment is arranged in such a way that the safety and the smoothness of the path planning are higher.
For example, a classifier, such as an SVM (Support Vector Machine), may be used to add a "good" label to a planned path that meets a preset path transformation requirement, and add a "bad" label to a planned path that does not meet the preset path transformation requirement or a path that fails to plan a path according to historical motion information of a vehicle or other vehicles. In this embodiment, only the planned path that meets the preset path transformation requirement, that is, the path of the type labeled "good" is taken as the target travel path, and the target travel path and the corresponding historical motion information of the own vehicle and the other vehicles form a training sample set to perform learning based on the neural network.
140. And training the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement.
The initial neural network model is preferably a fully-connected neural network, such as a deep neural network like a graph network or LSTM (Long Short-Term Memory network). The initial neural network model is trained by utilizing a training sample set, so that a target driving path when the path of the current vehicle is changed can be obtained, the target driving path is discretized according to time points, and each discrete point has a kinematic parameter value corresponding to the moment. According to the target driving path, the current vehicle can accurately, quickly and stably complete the path change.
According to the technical scheme provided by the embodiment, the planned path which meets the vehicle position constraint and enables the preset objective function to reach the preset convergence condition can be generated by utilizing historical motion information of the current vehicle and other vehicles, wherein the historical motion information comprises speed, position, acceleration and the like. The objective function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle, and the current vehicle can accurately, quickly and stably carry out path change when receiving a path change instruction by enabling the objective function to be minimum. In addition, the generated planning path is subjected to path transformation effect detection, so that a target driving path meeting the requirement of preset path transformation can be screened out, and the accuracy of path transformation is further improved. The target driving path and the corresponding historical motion information are used as a training sample set, the initial neural network model is trained by using the training sample set, a path transformation model of the vehicle can be obtained, the target driving path when the path is changed with the current vehicle can be obtained by using the path transformation model according to the motion information of the current vehicle and other vehicles, and compared with a mode that a search algorithm is adopted to screen out an optimal path from a plurality of candidate paths in the prior art, the method improves the accuracy and the rapidity of vehicle path transformation.
Example two
Referring to fig. 3, fig. 3 is a schematic flowchart of a method for changing a route of a vehicle according to an embodiment of the present invention. In this embodiment, on the basis that the path transformation model is constructed in the above embodiment, the method may be executed by a path transformation device of the vehicle, the device may be implemented in a software and/or hardware manner, and may be generally integrated in a vehicle-mounted terminal such as a vehicle-mounted Computer, a vehicle-mounted Industrial control Computer (IPC), and the like, and the embodiment of the present invention is not limited thereto. As shown in fig. 3, the method includes:
210. and acquiring environment perception information and a path transformation instruction.
The environment perception information comprises current motion information of other vehicles except the current vehicle, and the current motion information comprises; velocity, position and acceleration. The path change instruction may be an instruction sent by the driver to the driving assistance system according to the actual running condition of the current vehicle, or may be a path change instruction automatically triggered by the current automatic driving vehicle according to the navigation information, or may also be a path change instruction triggered by the current automatic driving vehicle when detecting that the running track of another vehicle will affect the running track of the current vehicle.
220. And according to the current motion information of the current vehicle and the current motion information of other vehicles, if a target driving path which is based on the path transformation model, generated and meets the vehicle position constraint and enables a preset target function to reach a preset convergence condition is obtained, executing path transformation operation according to the target driving path.
The objective function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle; the path conversion model associates current movement information of the current vehicle and other vehicles with a target travel path when the current vehicle performs path change. For the training process of the path transformation model, reference may be made to the contents provided in the foregoing embodiments, and details are not described in this embodiment again.
In the above embodiment, since the route conversion model is obtained by training using the motion information of the own vehicle and the other vehicle and the target travel route, the route conversion model has a function of obtaining the target travel route of the current vehicle from the motion information of the own vehicle and the other vehicle. In the actual application process of the path transformation model, if the target travel path can be acquired according to the current motion information of the current vehicle and the current motion information of other vehicles, the path transformation operation may be performed according to the target travel path.
For example, when the path transformation model is trained, the target driving path in the training sample set is a path which is obtained by screening after the detection of the path transformation effect and meets the preset path transformation requirement, that is, for some motion information, no corresponding target driving path exists. Therefore, if the corresponding target driving path cannot be acquired according to the current motion information of the current vehicle and the current motion information of other vehicles, the driving state of the current vehicle in the current lane is continuously maintained.
Further, before the path transformation operation is executed according to the target running path, the collision detection can be carried out on the current vehicle and other vehicles according to the kinematic parameter values corresponding to the discrete points of the target running path; if the detection result is that the current vehicle collides with other vehicles, keeping the running state of the current vehicle in the current lane; and if the detection result is that the current vehicle does not collide with other vehicles, executing the lane change action according to the target driving path. This arrangement can further enhance the safety of the lane path change.
The present embodiment applies the road transformation model to the actual path transformation process of the vehicle on the basis of the above-described embodiments. When the current vehicle receives a path transformation instruction, if a target driving path which is generated based on a path transformation model, meets vehicle position constraint and enables a preset target function to reach a preset convergence condition can be acquired according to the current motion information of the current vehicle and the current motion information of other vehicles, path transformation operation is executed according to the target driving path. Compared with the method that the target running path is generated in a continuous iteration mode directly according to the motion information of the own vehicle and other vehicles, the method for generating the target running path based on the neural network has the advantages that the generation speed of the target running path is increased, the method is simple, and the practicability is high.
EXAMPLE III
Referring to fig. 4, fig. 4 is a schematic structural diagram of a training device for a vehicle path transformation model according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes: a historical motion information acquisition module 310, a planned path generation module 320, a training sample set generation module 330 and a path transformation model establishment module 340; wherein the content of the first and second substances,
a historical motion information obtaining module 310 configured to obtain historical motion information of the current vehicle when the current vehicle receives the path change instruction, and historical motion information of other vehicles except the current vehicle in corresponding historical environmental perception information, wherein the historical motion information comprises speed, position and acceleration;
a planned path generating module 320 configured to generate a planned path that satisfies a vehicle position constraint and enables a preset objective function to reach a preset convergence condition according to the historical motion information of the own vehicle and the historical motion information of the other vehicle, wherein the objective function establishes an integral relationship among acceleration, a lateral position and time of a current vehicle;
a training sample set generating module 330, configured to perform detection of a path transformation effect on the planned path, and use a target driving path and corresponding historical motion information that meet a preset path transformation requirement in the detection result as a training sample set;
and the path transformation model establishing module 340 is configured to train the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model associates the motion information of the current vehicle and other vehicles with the target driving path when the current vehicle is subjected to path replacement.
Optionally, the planned path is generated in an iterative manner, and a kinematic parameter value meeting the vehicle position constraint, obtained each time according to the historical motion information of the own vehicle and the historical motion information of the other vehicles, is used as an input of the next iteration until the planned path with the minimum preset objective function is generated;
wherein the vehicle position constraint comprises:
when the current vehicle is in the current lane, the longitudinal position of the current vehicle is smaller than that of the front vehicle in the running direction;
when the current vehicle runs to a target lane after lane change is performed, the longitudinal position of the current vehicle is greater than the longitudinal position of the vehicle behind the current vehicle in the running direction and is less than the longitudinal position of the vehicle in front of the current vehicle;
the objective functionIs composed of
Wherein, c1Is a coefficient, a is acceleration, y is lateral position of the vehicle, ygoalThe lateral position of the vehicle in the target lane.
Optionally, a planned path that minimizes the preset objective function is generated according to the following iterative formula:
wherein the content of the first and second substances,
The training device for the vehicle path transformation model provided by the embodiment of the invention can execute the training method for the vehicle path transformation model provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in the above embodiments, reference may be made to a method for training a vehicle path transformation model provided in any embodiment of the present invention.
Example four
Referring to fig. 5, fig. 5 is a schematic structural diagram of a vehicle path transformation device according to an embodiment of the present invention, and as shown in fig. 5, the device includes: a path transformation instruction acquisition module 410 and a target driving path generation module 420; wherein the content of the first and second substances,
a path transformation instruction obtaining module 410 configured to receive environment perception information and a path transformation instruction, wherein the environment perception information includes current motion information of other vehicles except the current vehicle, and the current motion information includes; velocity, position and acceleration;
a target travel path generation module 420 configured to, according to the current motion information of the current vehicle and the current motion information of the other vehicle, if a target travel path that satisfies a vehicle position constraint and makes a preset target function reach a preset convergence condition based on a path transformation model is obtained, perform a path transformation operation according to the target travel path;
wherein the objective function establishes an integral relationship between the acceleration, lateral position and time of the current vehicle; the path transformation model associates current motion information of a current vehicle and other vehicles with a target travel path of the current vehicle when the current vehicle is subjected to path change.
Optionally, the apparatus further comprises:
and the running state keeping module is configured to keep the running state of the current vehicle in the current lane if the target running path is not acquired according to the current motion information of the current vehicle and the current motion information of the other vehicles.
Optionally, the apparatus further comprises:
the collision detection module is configured to perform collision detection on the current vehicle and the other vehicle according to the kinematic parameter values corresponding to the discrete points of the target driving path;
correspondingly, if the detection result shows that the current vehicle collides with other vehicles, the running state of the current vehicle in the current lane is kept;
and if the detection result shows that the current vehicle does not collide with other vehicles, executing the lane change action according to the target driving path.
Optionally, the path transformation model is constructed by:
acquiring historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
generating a planned path which meets vehicle position constraint and enables a preset target function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the target function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle;
detecting a path transformation effect of the planned path, and taking a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
and training the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement.
The vehicle path transformation device provided by the embodiment of the invention can execute the vehicle path transformation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in the above embodiments, reference may be made to a method for changing a route of a vehicle according to any embodiment of the present invention.
EXAMPLE five
Referring to fig. 6, fig. 6 is a schematic structural diagram of a vehicle-mounted terminal according to an embodiment of the present invention. As shown in fig. 6, the in-vehicle terminal may include:
a memory 701 in which executable program code is stored;
a processor 702 coupled to the memory 701;
the processor 702 calls the executable program code stored in the memory 701 to execute the training method of the vehicle path transformation model provided by any embodiment of the present invention.
The embodiment of the invention also provides another vehicle-mounted terminal which comprises a memory for storing executable program codes; a processor coupled to the memory; the processor calls the executable program codes stored in the memory to execute the vehicle path transformation method provided by any embodiment of the invention.
The embodiment of the invention discloses a computer-readable storage medium which stores a computer program, wherein the computer program enables a computer to execute the training method of the vehicle path transformation model provided by any embodiment of the invention.
The embodiment of the invention also discloses a computer readable storage medium which stores a computer program, wherein the computer program enables a computer to execute the path transformation method of the vehicle provided by any embodiment of the invention.
The embodiment of the invention discloses a computer program product, wherein when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps of the training method of the vehicle path transformation model provided by any embodiment of the invention.
Embodiments of the present invention also disclose a computer program product, wherein when the computer program product runs on a computer, the computer is caused to execute part or all of the steps of the method for path transformation of a vehicle provided by any of the embodiments of the present invention.
In various embodiments of the present invention, it should be understood that the sequence numbers of the above-mentioned processes do not imply a necessary order of execution, and the order of execution of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In the embodiments provided herein, it should be understood that "B corresponding to A" means that B is associated with A from which B can be determined. It should also be understood, however, that determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present invention, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of each embodiment of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the methods of the above embodiments may be implemented by program instructions associated with hardware, and the program may be stored in a computer-readable storage medium, wherein the storage medium includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM), or other Memory, disk Memory, or other storage device, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
The training method, the path transformation method and the device for the vehicle path transformation model disclosed by the embodiment of the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (9)
1. A training method of a vehicle path transformation model is characterized by comprising the following steps:
acquiring historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
generating a planned path which meets vehicle position constraint and enables a preset target function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the target function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle; wherein the objective function is
Wherein, c1Is a coefficient, a is acceleration, y is lateral position of the vehicle, ygoalThe transverse position of the vehicle in the target lane;
detecting a path transformation effect of the planned path, and taking a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
training an initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement;
generating a planned path in an iterative mode, wherein kinematic parameter values meeting the vehicle position constraint, which are obtained each time according to the historical motion information of the self vehicle and the historical motion information of other vehicles, are used as input of next iteration until the planned path which enables a preset objective function to be minimum is generated;
generating a planning path which enables a preset target function to be minimum according to the following iterative formula:
wherein the content of the first and second substances,
wherein x represents the longitudinal position of the current vehicle and y represents the lateral position of the current vehicle; v represents the current vehicle speed; θ represents a turning angle of the current vehicle; n represents the nth discrete point of the planning track, and k represents the iteration number; v. ofminAnd vmaxRespectively representing the lowest speed and the highest speed of the current vehicle; w is aegoIndicating the width of the current vehicle;
indicating a longitudinal position of an m-th following vehicle behind the current vehicle in the running direction when the vehicle travels to the target lane after performing the lane change;
indicating a longitudinal position of an ith vehicle ahead of the current vehicle in the running direction when the current vehicle travels to the target lane after performing the lane change; deltasafe(i)、Δsafe(m)Respectively representing the safe distances between the current vehicle and the ith front vehicle and the mth rear vehicle.
2. The method of claim 1, wherein the vehicle position constraint comprises:
when the current vehicle is in the current lane, the longitudinal position of the current vehicle is smaller than that of the front vehicle in the running direction;
when the current vehicle travels to a target lane after performing lane change, the longitudinal position of the current vehicle is greater than the longitudinal position of the vehicle behind the current vehicle in the travel direction and less than the longitudinal position of the vehicle in front of the current vehicle.
3. A method of changing a path of a vehicle, comprising:
receiving environment perception information and a path transformation instruction, wherein the environment perception information comprises current motion information of other vehicles except a current vehicle, and the current motion information comprises; speed, position and acceleration;
according to the current motion information of the current vehicle and the current motion information of other vehicles, if a target driving path which is generated based on a path transformation model, meets vehicle position constraint and enables a preset target function to reach a preset convergence condition is obtained, path transformation operation is executed according to the target driving path;
wherein the objective function establishes an integral relationship between the acceleration, lateral position and time of the current vehicle; the path transformation model enables current motion information of a current vehicle and other vehicles to be associated with a target driving path when the current vehicle carries out path replacement; wherein the objective function is
Wherein, c1Is a coefficient, a is an accelerationY is the lateral position of the vehicle, ygoalThe transverse position of the vehicle in the target lane;
generating a planned path in an iterative mode, and taking a kinematic parameter value meeting the vehicle position constraint, which is obtained each time according to historical motion information of a vehicle and historical motion information of other vehicles, as an input of next iteration until the planned path with the minimum preset objective function is generated;
generating a planning path which enables a preset target function to be minimum according to the following iterative formula:
wherein the content of the first and second substances,
wherein x represents the longitudinal position of the current vehicle and y represents the lateral position of the current vehicle; v represents the current vehicle speed; θ represents a turning angle of the current vehicle; n represents the nth discrete point of the planning track, and k represents the iteration number; v. ofminAnd vmaxRespectively representing the lowest speed and the highest speed of the current vehicle; w is aegoIndicating the width of the current vehicle;
indicating a longitudinal position of an m-th rear vehicle behind the current vehicle in the running direction when the vehicle travels to the target lane after performing the lane change;
indicating a longitudinal position of an ith vehicle ahead of the current vehicle in the running direction when the current vehicle travels to the target lane after performing the lane change; deltasafe(i)、Δsafe(m)Respectively represent the current vehicle anda safety distance between the ith preceding vehicle and the mth following vehicle.
4. The method of claim 3, further comprising:
and according to the current motion information of the current vehicle and the current motion information of the other vehicles, if the target driving path is not acquired, keeping the driving state of the current vehicle in the current lane.
5. The method according to claim 3 or 4, wherein before performing a path change operation according to the target travel path, the method further comprises:
according to the kinematic parameter values corresponding to the discrete points of the target driving path, performing collision detection on the current vehicle and the other vehicles;
correspondingly, if the detection result shows that the current vehicle collides with the other vehicle, keeping the running state of the current vehicle in the current lane;
and if the detection result shows that the current vehicle does not collide with other vehicles, executing the lane change action according to the target driving path.
6. The method according to any of claims 3-5, wherein the path transformation model is constructed by:
acquiring historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
generating a planned path which meets vehicle position constraint and enables a preset target function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the target function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle;
detecting a path transformation effect of the planned path, and taking a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
and training the initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, wherein the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement.
7. A training apparatus for a vehicle path transformation model, comprising:
the historical motion information acquisition module is configured to acquire historical motion information of a current vehicle when the current vehicle receives a path change instruction and historical motion information of other vehicles except the current vehicle in corresponding historical environment perception information, wherein the historical motion information comprises speed, position and acceleration;
the planned path generating module is configured to generate a planned path which meets vehicle position constraint and enables a preset objective function to reach a preset convergence condition according to the historical motion information of the self vehicle and the historical motion information of the other vehicles, wherein the objective function establishes an integral relation among the acceleration, the transverse position and the time of the current vehicle; wherein the objective function is
Wherein, c1Is a coefficient, a is acceleration, y is lateral position of the vehicle, ygoalThe transverse position of the vehicle in the target lane;
the training sample set generation module is configured to detect a path transformation effect of the planned path, and takes a target driving path meeting a preset path transformation requirement in a detection result and corresponding historical motion information as a training sample set;
the path transformation model establishing module is configured to train an initial neural network model by using the training sample set to obtain a path transformation model of the vehicle, and the path transformation model enables the motion information of the current vehicle and other vehicles to be associated with a target driving path when the current vehicle is subjected to path replacement;
generating a planned path in an iterative mode, wherein kinematic parameter values meeting the vehicle position constraint, which are obtained each time according to the historical motion information of the self vehicle and the historical motion information of other vehicles, are used as input of next iteration until the planned path which enables a preset objective function to be minimum is generated;
generating a planning path which enables a preset target function to be minimum according to the following iterative formula:
wherein, the first and the second end of the pipe are connected with each other,
wherein x represents the longitudinal position of the current vehicle and y represents the lateral position of the current vehicle; v represents the current vehicle speed; θ represents a turning angle of the current vehicle; n represents the nth discrete point of the planning track, and k represents the iteration number; v. ofminAnd vmaxRespectively representing the lowest speed and the highest speed of the current vehicle; w is aegoIndicating the width of the current vehicle;
indicating a longitudinal position of an m-th following vehicle behind the current vehicle in the running direction when the vehicle travels to the target lane after performing the lane change;
indicating a longitudinal position of an ith vehicle ahead of the current vehicle in the running direction when the current vehicle travels to the target lane after performing the lane change; deltasafe(i)、Δsafe(m)Respectively show whenA safe distance between the preceding vehicle and the ith preceding vehicle and the mth following vehicle.
8. The apparatus of claim 7,
the vehicle position constraint includes:
when the current vehicle is in the current lane, the longitudinal position of the current vehicle is smaller than that of the front vehicle in the running direction;
when the current vehicle travels to a target lane after performing lane change, the longitudinal position of the current vehicle is greater than the longitudinal position of the vehicle behind the current vehicle in the travel direction and less than the longitudinal position of the vehicle in front of the current vehicle.
9. A path conversion apparatus of a vehicle, characterized by comprising:
the system comprises a path transformation instruction acquisition module, a path transformation instruction acquisition module and a path transformation instruction processing module, wherein the path transformation instruction acquisition module is configured to receive environment perception information and a path transformation instruction, the environment perception information comprises current motion information of other vehicles except a current vehicle, and the current motion information comprises; speed, position and acceleration;
a target driving path generating module configured to execute a path transformation operation according to a target driving path, which satisfies a vehicle position constraint and makes a preset target function reach a preset convergence condition, if the target driving path is acquired based on a path transformation model according to the current motion information of the current vehicle and the current motion information of the other vehicle;
wherein the objective function establishes an integral relationship between the acceleration, lateral position and time of the current vehicle; the path transformation model enables current motion information of a current vehicle and other vehicles to be associated with a target driving path when the current vehicle carries out path replacement; wherein the objective function is
Wherein, c1Is a coefficient, a is acceleration, y is lateral position of the vehicle, ygoalFor vehicles in the target vehicleThe lateral position of the track;
generating a planned path in an iterative mode, and taking a kinematic parameter value meeting the vehicle position constraint, which is obtained each time according to historical motion information of a vehicle and historical motion information of other vehicles, as an input of next iteration until the planned path with the minimum preset objective function is generated;
the planning path which enables the preset objective function to be minimum is generated according to the following iterative formula:
wherein the content of the first and second substances,
wherein x represents the longitudinal position of the current vehicle and y represents the lateral position of the current vehicle; v represents the current vehicle speed; θ represents a turning angle of the current vehicle; n represents the nth discrete point of the planning track, and k represents the iteration number; v. ofminAnd vmaxRespectively representing the lowest speed and the highest speed of the current vehicle; w is aegoIndicating the width of the current vehicle;
indicating a longitudinal position of an m-th following vehicle behind the current vehicle in the running direction when the vehicle travels to the target lane after performing the lane change;
indicating a longitudinal position of an ith vehicle ahead of the current vehicle in the running direction when the current vehicle travels to the target lane after performing the lane change; deltasafe(i)、Δsafe(m)Respectively representing the safe distances between the current vehicle and the ith front vehicle and the mth rear vehicle.
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