CN115727864B - Path prediction method based on visual navigation vehicle and related equipment - Google Patents
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Abstract
The application provides a route prediction method and related equipment based on a visual navigation vehicle, wherein the route prediction method of a vehicle automatic driving system based on combination of driving reference track recognition and driving local route end point prediction based on camera visual perception can accurately predict and plan a smooth driving route for the vehicle in real time, and improve the anti-interference performance of the vehicle on road conditions, so that the method has good riding comfort, avoids bad results caused by abnormal detection of the reference track, and realizes stable and reliable overbending and track change. In addition, the path prediction planning algorithm can plan a smooth driving path from the current position of the vehicle to the reference track when the vehicle deviates from the reference track greatly, so that the automatic driving system has automatic deviation correcting capability.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a route prediction method and related equipment based on a visual navigation vehicle.
Background
When the automatic driving vehicle tracks on the transition section of the reference track straight-way changing curve or the curved-way changing straight-way and the reference track turnout transition section, the phenomenon of steering abrupt change often exists, and even the phenomenon of over-bending or the phenomenon of track changing cannot be smoothly performed. The main factors causing the phenomenon include certain deviation of reference track construction, partial abrasion in the use process, and increased curve curvature, so that the quality of track curvature data collected by a front-mounted camera of a vehicle is poor or the detection range of the camera suddenly drops, and the sensing errors can lead an automatic driving system to be subjected to undesired steering control, and even lead the vehicle to drive out of a lane boundary. The current safety precaution is that when the above event occurs, the autopilot system will require the driver to hold the steering wheel and prepare to take over steering control in a short period of time. However, the workload of the driver is increased, and the trust degree of the driver on the automatic driving system is reduced.
Disclosure of Invention
In view of the above, the present application is directed to a path prediction method and related apparatus for a vehicle based on visual navigation.
Based on the above object, the present application provides a path prediction method based on a visual navigation vehicle, comprising:
constructing an orbital equation coordinate system by taking a vehicle-mounted camera as an origin, wherein the positive direction of the x-axis of the coordinate system is perpendicular to the headstock and points to the running direction of the vehicle, and the positive direction of the y-axis of the coordinate system is parallel to the headstock and points to the left side of the running direction of the vehicle;
the vehicle runs along a global reference track, the camera acquires local reference track information for multiple times within the range of the running distance being the maximum perceived longitudinal distance of the camera, and a plurality of track line equations are constructed based on the local reference track information acquired for multiple times;
determining a tracking path starting point based on the track equation coordinate system where the track line equation corresponding to the local reference track information acquired last time is located, and obtaining a plurality of groups of state constant values by carrying out translation and rotation mapping on a plurality of track line equations;
based on the track line equation corresponding to the local reference track information acquired last time and a plurality of track line equations subjected to translation and rotation mapping, estimating the end point of the tracking path through calculation to obtain a plurality of estimated end points;
Calculating a tracking path end point based on a plurality of estimated end points;
Determining a tracking path curve based on the tracking path start point and the tracking path end point;
And iteratively updating a plurality of groups of state constant values based on the tracking path curve to generate the tracking path curve of the next period.
Further, the vehicle travels along the global reference track, the camera acquires local reference track information for a plurality of times within a range that a travel distance is a maximum perceived longitudinal distance of the camera, and constructs a plurality of track line equations based on the local reference track information acquired for a plurality of times, including:
The vehicle obtains the local reference track information once through the camera after driving for a preset distance within the maximum perception longitudinal distance range of the camera, and the vehicle obtains at least N pieces of local reference track information within the range to construct N pieces of track line equations, wherein N is expressed as
N is a positive integer, floor represents downward rounding operation, L m represents the maximum perceived longitudinal distance of the camera, and Ls represents the pre-aiming distance.
Further, the determining the start point of the tracking path based on the coordinate system of the track equation where the track line equation corresponding to the local reference track information acquired last time is located, and obtaining a plurality of groups of state constant values by performing translation and rotation mapping on a plurality of track line equations includes:
According to the sequence of the local reference orbit information acquisition, the local reference orbit information acquired in the last time corresponds to the Nth orbit line equation, the origin of a coordinate system of the Nth orbit line equation is determined to be the starting point of the tracking path, and the previous N-1 orbit line equations are respectively mapped into the coordinate system of the Nth orbit line equation through translation and rotation to obtain N-1 sets of state constant values.
Further, the state constant value is expressed as (x 0,y0,θ),x0 and y 0 represent coordinates of an origin of the r-th orbital equation coordinate system in the r-1 th orbital equation coordinate system, θ represents an angle between an x-axis of the r-th orbital equation coordinate system and an x-axis of the r-1 th orbital equation coordinate system, and 1<r is less than or equal to N.
Further, the calculating the tracking path end point based on the estimated end points includes:
Calculating a plurality of estimated endpoints based on the estimated endpoints to obtain a plurality of tracking path predicted endpoints, and calculating an exponential weighting fusion algorithm based on the plurality of tracking path predicted endpoints to obtain the tracking path endpoint.
Further, the determining a tracking path curve based on the tracking path start point and the tracking path end point includes:
The tracking path curve is determined by constructing a unitary cubic equation in the coordinate system of the nth track line equation based on the tracking path start point and the tracking path end point.
Further, the iteratively updating the plurality of sets of state constant values based on the tracking path curve to generate the tracking path curve of the next cycle includes:
extracting parameters from the tracking path curve to replace the state constant values of the N-1 th group as updated state constant values of the N-1 th group, sequentially taking the state constant values of the s-1 st group as new state constant values of the s-2 nd group, and generating the tracking path curve of the next section based on the updated state constant values of the N-1 th group, wherein s is more than or equal to 3 and less than or equal to N.
Based on the same inventive concept, the application also provides a path prediction method device based on the visual navigation vehicle, which comprises the following steps:
the coordinate system construction module is configured to construct an orbit equation coordinate system by taking the vehicle-mounted camera as an origin, wherein the positive direction of the x-axis of the coordinate system is perpendicular to the headstock and points to the running direction of the vehicle, and the positive direction of the y-axis of the coordinate system is parallel to the headstock and points to the global reference orbit direction;
the equation construction module is configured to enable the vehicle to run along the global reference track, acquire local reference track information for multiple times by the camera within the range that the running distance is the maximum perceived longitudinal distance of the camera, and construct a plurality of track line equations based on the local reference track information acquired multiple times;
The mapping module is configured to determine a tracking path starting point based on the track equation coordinate system where the track line equation corresponding to the local reference track information acquired last time is located, and a plurality of groups of state constant values are obtained by carrying out translation and rotation mapping on a plurality of track line equations;
The terminal point estimating module is configured to estimate the terminal point of the tracking path through calculation based on the track line equation corresponding to the local reference track information acquired last time and a plurality of track line equations subjected to translation and rotation mapping, so as to obtain a plurality of estimated terminal points;
the end point determining module is configured to obtain a tracking path end point through calculation based on a plurality of estimated end points;
A path generation module configured to determine a tracking path curve based on the tracking path start point and the tracking path end point;
and the iteration updating module is configured to carry out iteration updating on a plurality of groups of state constant values based on the tracking path curve so as to generate the tracking path curve of the next period.
From the above, it can be seen that the route prediction method and the related device for a vision navigation-based vehicle provided by the application can accurately predict and plan a smooth driving route for a vehicle in real time by using the route prediction method for the vehicle automatic driving system based on the combination of the driving reference track recognition and the driving local route end prediction based on the camera vision perception, and improve the anti-interference performance of the vehicle on road conditions, thereby having good riding comfort, avoiding the bad results caused by the abnormal detection of the reference track, and realizing stable and reliable overbending and track change. In addition, the path prediction planning algorithm can plan a smooth driving path from the current position of the vehicle to the reference track when the vehicle deviates from the reference track greatly, so that the automatic driving system has automatic deviation correcting capability.
Based on the same inventive concept, the application also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method as described above when executing the computer program.
Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described above.
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In order to more clearly illustrate the technical solutions of the present application or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort to those of ordinary skill in the art.
FIG. 1 is a flow chart of a method for predicting a path of a vehicle based on visual navigation according to an embodiment of the present application;
FIG. 2 is a schematic view of the visual perception of a reference track according to an embodiment of the present application;
FIG. 3 is a schematic diagram of N local reference track lines according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a tracking path end point prediction according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a tracking path curve according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a path prediction apparatus for a visually-guided vehicle according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
Embodiments of the present application are described in detail below with reference to the accompanying drawings.
The application provides a path prediction method based on a visual navigation vehicle, which comprises the following steps with reference to fig. 1:
and S101, constructing an orbital equation coordinate system by taking the vehicle-mounted camera as an origin, wherein the positive direction of the x-axis of the coordinate system is perpendicular to the vehicle head and points to the running direction of the vehicle, and the positive direction of the y-axis of the coordinate system is parallel to the vehicle head and points to the left side of the running direction of the vehicle.
Specifically, the visual perception of the front camera of the autonomous vehicle for the global reference track is shown in fig. 2. The local reference track line identified by the camera is a solid line segment part in the global reference track, and the equation of the local reference track line is as follows:
y(x)＝C3x3+C2x2+C1x+C0,0≤x≤Lm
Wherein x is the longitudinal distance of the track line relative to the camera, the x-axis of the coordinate system is perpendicular to the headstock, y is the transverse distance of the track line relative to the camera, and the y-axis of the coordinate system is parallel to the headstock. C 3、C2、C1、C0 is the third order term coefficient, the second order term coefficient, the first order term coefficient and the zero order term coefficient of the equation respectively, and L m is the maximum effective longitudinal distance identifiable by the camera.
Step S102, the vehicle runs along the global reference track, the camera acquires local reference track information for a plurality of times within the range that the running distance is the maximum perceived longitudinal distance of the camera, and a plurality of track line equations are constructed based on the local reference track information acquired for a plurality of times.
Specifically, in the process that the vehicle runs along the global reference track, the local reference track information is acquired through the camera at intervals, and the running distance of the vehicle does not exceed the maximum perceived longitudinal distance L m of the camera. In the running process, local reference orbit information is acquired for multiple times, an orbit line equation is constructed according to the acquired local reference orbit information, and a plurality of orbit line equations are constructed based on the local reference orbit information.
Step S103, determining a tracking path starting point based on the track equation coordinate system where the track line equation corresponding to the local reference track information acquired last time is located, and obtaining a plurality of groups of state constant values through translation and rotation mapping of a plurality of track line equations.
Specifically, multiple times of local reference orbit information is acquired within the distance range of the vehicle running L m, the starting point of the tracking path is determined based on an orbit equation coordinate system to which an orbit line equation constructed by the finally sequentially acquired local reference orbit information belongs, and meanwhile, translation and rotation mapping is performed on the multiple orbit line equations, and a set of state constant values are obtained after each translation and rotation mapping is performed.
Step S104, estimating the end point of the tracking path through calculation based on the track line equation corresponding to the local reference track information acquired last time and a plurality of track line equations subjected to translation and rotation mapping, and obtaining a plurality of estimated end points.
Specifically, translation and rotation are performed on the acquired track line equations and mapped to a coordinate system to which the track line equation finally acquired belongs, an estimated endpoint is obtained by calculating each track line equation subjected to translation and rotation mapping, and meanwhile, an estimated endpoint is also obtained by calculating the track line equation finally acquired, and the estimated endpoints are used for calculating the tracking path endpoint.
And step 105, calculating to obtain a tracking path end point based on a plurality of estimated end points. And obtaining a final tracking path end point, namely a predicted end point of vehicle running in a range by carrying out fusion calculation on the plurality of estimated end points.
And step S106, determining a tracking path curve based on the tracking path starting point and the tracking path ending point. The starting point and the end point of the tracking path are obtained through calculation, and a tracking path curve is obtained through calculation based on the starting point and the end point by constructing a curve equation.
And step S107, carrying out iterative updating on a plurality of groups of state constant values based on the tracking path curve so as to generate the tracking path curve of the next period. And after the first section of tracking path curve is obtained, carrying out iterative updating on a plurality of groups of state constant values to obtain a plurality of groups of new state constant values, calculating based on the plurality of groups of new state constant values to obtain a second section of tracking path curve, and the like, and calculating the rest tracking path curves.
In some embodiments, the vehicle travels along the global reference track, the camera acquires local reference track information multiple times within a range of travel distances being a maximum perceived longitudinal distance of the camera, and constructs a plurality of track line equations based on the local reference track information acquired multiple times, including:
The vehicle obtains the local reference track information once through the camera after driving for a preset distance within the maximum perception longitudinal distance range of the camera, and the vehicle obtains at least N pieces of local reference track information within the range to construct N pieces of track line equations, wherein N is expressed as
N is a positive integer, floor represents downward rounding operation, L m represents the maximum perceived longitudinal distance of the camera, and Ls represents the pre-aiming distance.
Specifically, referring to fig. 3, after the vehicle travels the distance L m, N times of local reference orbit information is obtained through the camera, and N orbit line equations are constructed. Respectively is
The orbit line equations respectively correspond to different orbit equation coordinate systems, namely an x 0-y0 coordinate system, an x 1-y1 coordinate system, an x n-yn coordinate system and an x n-1-yn-1 coordinate system.
In some embodiments, the determining the start point of the tracking path based on the coordinate system of the track equation where the track line equation corresponding to the local reference track information acquired last time is located, and performing translation and rotation mapping on a plurality of track line equations to obtain a plurality of sets of state constant values includes:
According to the sequence of the local reference orbit information acquisition, the local reference orbit information acquired in the last time corresponds to the Nth orbit line equation, the origin of a coordinate system of the Nth orbit line equation is determined to be the starting point of the tracking path, and the previous N-1 orbit line equations are respectively mapped into the coordinate system of the Nth orbit line equation through translation and rotation to obtain N-1 sets of state constant values.
In some embodiments, the state constant value is represented as (x k,yk, θ), where x k and y k represent coordinates of an origin of the r-th said orbital equation coordinate system in the r-1 th said orbital equation coordinate system, θ represents an angle between an x-axis of the r-th said orbital equation coordinate system and an x-axis of the r-1 th said orbital equation coordinate system, 1<r.ltoreq.N.
Specifically, as shown in fig. 3, the local reference orbit line equation in the x 0-y0 coordinate system satisfies:
Mapping the orbital line equation in the x 1-y1 coordinate system into the x 0-y0 coordinate system through translation and rotation can obtain:
Wherein, (x o1,yo1) is the coordinate of the origin of the x 0-y0 coordinate system in the x 1-y1 coordinate system, θ 1 is the angle between the x 0 axis in the x 0-y0 coordinate system and the x 1 axis in the x 1-y1 coordinate system, and (x o1,yo1,θ1) is a set of state constant values of the autonomous vehicle corresponding to the x 1-y1 coordinate system.
Similarly, the orbital line equation in the x n-yn coordinate system is mapped into the x 0-y0 coordinate system through translation and rotation, so that the following steps are obtained:
Where n is a positive integer, n ε [1, N-1], (x on,yon) is the coordinate of the origin of the x n-1-yn-1 coordinate system in the x n-yn coordinate system, θ n is the angle between the x n-1 axis in the x n-yn coordinate system and the x n axis in the x n-1-yn-1 coordinate system, and (x on,yon,θn) is a set of state constant values of the autonomous vehicle corresponding to the x n-yn coordinate system.
Finally, the orbital line equation in the x N-1-yN-1 coordinate system is mapped into the x 0-y0 coordinate system through translation and rotation, and the following steps are obtained:
Wherein, (x o(N-1),yo(N-1)) is the coordinate of the origin of the x N-2-yN-2 coordinate system in the x N-1-yN-1 coordinate system, θ N-1 is the angle between the x N-2 axis in the x N-2-yN-2 coordinate system and the x N-1 axis in the x N-1-yN-1 coordinate system, and (x o(N-1),yo(N-1),θN-1) is a set of state constant values of the autonomous vehicle corresponding to the x N-1-yN-1 coordinate system.
In some embodiments, the calculating the tracking path end point based on the plurality of estimated end points includes:
Calculating a plurality of estimated endpoints based on the estimated endpoints to obtain a plurality of tracking path predicted endpoints, and calculating an exponential weighting fusion algorithm based on the plurality of tracking path predicted endpoints to obtain the tracking path endpoint.
In order to further ensure the accuracy and smoothness of the tracking path, the above N track line equations are adopted to estimate the end point of the tracking path corresponding to the distance of the vehicle driving Ls along the x 0 axis direction in the x 0-y0 coordinate system, so as to plan the tracking path curve, as shown in fig. 4.
The origin (x s,ys) of the tracking path is the origin of the x 0-y0 coordinate system, satisfying the following equation:
the end point of the tracking path can be estimated by N track lines corresponding to formulas (1.1) - (1.4), thereby obtaining the tracking path according to formula (1.1)
Where (x e0,ye0) is the equation curve y 0(x0) the estimated tracking path end point under the x 0-y0 coordinate system. Similarly, according to formula (1.2)
Where (x e1,ye1) is the equation curve y 1(x1) the estimated tracking path end point under the x 0-y0 coordinate system. Obtainable according to formula (1.3)
Where (x en,yen) is the equation curve y n(xn) the estimated tracking path end point under the x 0-y0 coordinate system. Obtained according to formula (1.4)
Where (x e(N-1),ye(N-1)) is the equation curve y N-1(xN-1) the estimated tracking path end point under the x 0-y0 coordinate system. N estimated endpoints (x e0,ye0)、(xe1,ye1)…(xen,yen)…(xe(N-1),ye(N-1)) were obtained from formulas (1.6) to (1.9).
In some embodiments, the calculating the tracking path end point based on the plurality of estimated end points includes:
Calculating a plurality of estimated endpoints based on the estimated endpoints to obtain a plurality of tracking path predicted endpoints, and calculating an exponential weighting fusion algorithm based on the plurality of tracking path predicted endpoints to obtain the tracking path endpoint.
By substituting the first two equations of equation (1.8) into the corresponding orbital line equation, i.e., into the third equation, a unitary cubic equation for x n can be obtained
Wherein the method comprises the steps of
Solving the formula (1.10) to obtain a real solution of x n
Further obtain according to formula (1.8)
The latter three equations of equation (1.8) are used to obtain
Thereby obtaining N tracking path prediction end points
Carrying out exponential weighting fusion on N tracking path prediction end points in the formula (1.15) to obtain a final tracking path end point (x e,ye)
Wherein alpha is E (0, 1), and the value is specifically taken according to the actual vehicle experiment.
In some embodiments, the determining a tracking path curve based on the start point of the tracking path and the tracking path end point includes:
the tracking path curve is determined by constructing a unitary cubic equation in the coordinate system of the nth track line equation based on the start point of the tracking path and the tracking path end point.
Specifically, a unitary cubic polynomial equation is selected to perform the planning of the tracking path curve in the x 0-y0 coordinate system, specifically
Wherein A 3、A2、A1、A0 is the third order coefficient, the second order coefficient, the first order coefficient and the zero order coefficient of the equation, respectively. According to the formulas (1.5) and (1.16), the states of the start point (x s,ys) and the end point (x e,ye) of the tracking path curve are considered to satisfy
Wherein y p represents the solution of the equation in the y-axis direction in equation (1.17). The formula (1.18) is brought into the formula (1.17) to obtain
Solving the formula (1.19) to obtain
Thus equation (1.17) can be expressed as
/>
According to the formula (1.21), a tracking path curve planned in the x 0-y0 coordinate system is obtained as shown in fig. 5, in which the solid line portion represents the tracking path curve.
In some embodiments, the iteratively updating the plurality of sets of the state constant values based on the tracking path curve to generate the tracking path curve for a next cycle includes:
Extracting parameters from the tracking path curve to replace the state constant values of the N-1 th group as updated state constant values of the N-1 st group, sequentially taking the state constant values of the s-1 st group as new state constant values of the s-2 nd group, and generating the tracking path curve of the next period based on updating all the state constant values, wherein s is more than or equal to 3 and less than or equal to N.
Specifically, the state constant values obtained in the above embodiment are (xo1,yo1,θ1)、(xo2,yo2,θ2)…(xon,yon,θn)…(xo(N-1),yo(N-1),θN-1),, and all the state constant values are iteratively updated based on the tracking path curve, specifically, the state constant values are iteratively updated from left to right according to the following expression, which is used for generating the tracking path curve of the next cycle
The state constant value (x o1,yo1,θ1) is replaced by (L s,ye,arctan(ye'), the state constant value (x o2,yo2,θ2) is replaced by (x o1,yo1,θ1), the line replacement is updated, the tracking path curve of the next period is calculated and generated based on all updated state constant values, and then all state constant values are iteratively updated to calculate and generate the subsequent tracking path curve.
It should be noted that, the method of the embodiment of the present application may be performed by a single device, for example, a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the method of an embodiment of the present application, the devices interacting with each other to accomplish the method.
It should be noted that the foregoing describes some embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, the application also provides a path prediction method device based on the visual navigation vehicle, which corresponds to the method of any embodiment.
Referring to fig. 6, the path prediction method and apparatus based on the visual navigation vehicle include:
The coordinate system construction module 601 is configured to construct an orbital equation coordinate system by taking the vehicle-mounted camera as an origin, wherein the positive x-axis direction of the coordinate system is perpendicular to the headstock and points to the running direction of the vehicle, and the positive y-axis direction of the coordinate system is parallel to the headstock and points to the left side of the running direction of the vehicle;
An equation construction module 602, configured to drive a vehicle along the global reference track, wherein the driving distance is within a range of a maximum perceived longitudinal distance of the camera, the camera acquires local reference track information multiple times, and constructs a plurality of track line equations based on the local reference track information acquired multiple times;
The mapping module 603 is configured to determine a tracking path starting point based on the orbital equation coordinate system where the orbital line equation corresponding to the local reference orbit information acquired last time is located, and obtain a plurality of groups of state constant values by performing translation and rotation mapping on a plurality of the orbital line equations;
The end point estimating module 604 is configured to estimate the end point of the tracking path by calculation based on the track line equation corresponding to the local reference track information acquired last time and a plurality of track line equations mapped by translation and rotation, so as to obtain a plurality of estimated end points;
an end point determination module 605 configured to obtain a tracking path end point by calculation based on a plurality of the estimated end points;
a path generation module 606 configured to determine a tracking path curve based on the tracking path start point and the tracking path end point;
An iterative updating module 607 configured to iteratively update a plurality of sets of the state constant values based on the tracking path curve to generate the tracking path curve for a next cycle.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
The device of the foregoing embodiment is configured to implement the corresponding route prediction method based on the visual navigation vehicle in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the application also provides an electronic device corresponding to the method of any embodiment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the path prediction method based on the visual navigation vehicle according to any embodiment.
Fig. 7 is a schematic diagram of a hardware structure of an electronic device according to the embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage, dynamic storage, etc. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown in the figure) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the corresponding route prediction method based on the visual navigation vehicle in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the present application also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the visual navigation vehicle-based path prediction method according to any of the above embodiments, corresponding to any of the above embodiments.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The storage medium of the above embodiment stores computer instructions for causing the computer to perform the path prediction method based on the visual navigation vehicle according to any one of the above embodiments, and has the advantages of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the application, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the application as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present application, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, and the like, which are within the spirit and principles of the embodiments of the application, are intended to be included within the scope of the application.
Claims (10)
1. A method for predicting a path based on a visual navigation vehicle, comprising:
constructing an orbital equation coordinate system by taking a vehicle-mounted camera as an origin, wherein the positive direction of the x-axis of the coordinate system is perpendicular to the headstock and points to the running direction of the vehicle, and the positive direction of the y-axis of the coordinate system is parallel to the headstock and points to the left side of the running direction of the vehicle;
the vehicle runs along a global reference track, the camera acquires local reference track information for multiple times within the range of the running distance being the maximum perceived longitudinal distance of the camera, and a plurality of track line equations are constructed based on the local reference track information acquired for multiple times;
determining a tracking path starting point based on the track equation coordinate system where the track line equation corresponding to the local reference track information acquired last time is located, and obtaining a plurality of groups of state constant values by carrying out translation and rotation mapping on a plurality of track line equations;
based on the track line equation corresponding to the local reference track information acquired last time and a plurality of track line equations subjected to translation and rotation mapping, estimating the end point of the tracking path through calculation to obtain a plurality of estimated end points;
Calculating a tracking path end point based on a plurality of estimated end points;
Determining a tracking path curve based on the tracking path start point and the tracking path end point;
And iteratively updating a plurality of groups of state constant values based on the tracking path curve to generate the tracking path curve of the next period.
2. The path prediction method according to claim 1, wherein the vehicle travels along the global reference track, the camera acquires local reference track information a plurality of times within a range in which a travel distance is a maximum perceived longitudinal distance of the camera, and constructs a plurality of track line equations based on the local reference track information acquired a plurality of times, comprising:
The vehicle obtains the local reference track information once through the camera after driving for a preset distance within the maximum perception longitudinal distance range of the camera, and the vehicle obtains at least N pieces of local reference track information within the range to construct N pieces of track line equations, wherein N is expressed as
N is a positive integer, floor represents downward rounding operation, L m represents the maximum perceived longitudinal distance of the camera, and Ls represents the pre-aiming distance.
3. The method of claim 2, wherein determining a tracking path starting point based on the orbital equation coordinate system in which the orbital line equation corresponding to the local reference orbit information acquired last time is located, and obtaining a plurality of sets of state constant values by performing translation and rotation mapping on a plurality of the orbital line equations includes:
According to the sequence of the local reference orbit information acquisition, the local reference orbit information acquired in the last time corresponds to the Nth orbit line equation, the origin of a coordinate system of the Nth orbit line equation is determined to be the starting point of the tracking path, and the previous N-1 orbit line equations are respectively mapped into the coordinate system of the Nth orbit line equation through translation and rotation to obtain N-1 sets of state constant values.
4. The path prediction method according to claim 2, wherein the state constant value is expressed as (x k,yk, θ), wherein x k and y k represent coordinates of an origin of the r-th one of the orbital equation coordinate systems in the r-1 th one of the orbital equation coordinate systems, θ represents an angle between an x-axis of the r-th one of the orbital equation coordinate systems and an x-axis of the r-1 th one of the orbital equation coordinate systems, 1<r.ltoreq.n.
5. The path prediction method according to claim 1, wherein the calculating a tracking path end point based on a plurality of the estimated end points includes:
Calculating a plurality of estimated endpoints based on the estimated endpoints to obtain a plurality of tracking path predicted endpoints, and calculating an exponential weighting fusion algorithm based on the plurality of tracking path predicted endpoints to obtain the tracking path endpoint.
6. The path prediction method according to claim 2, wherein the determining a tracking path curve based on the tracking path start point and the tracking path end point includes:
The tracking path curve is determined by constructing a unitary cubic equation in the coordinate system of the nth track line equation based on the tracking path start point and the tracking path end point.
7. The path prediction method according to claim 3, wherein the iteratively updating the plurality of sets of the state constant values based on the tracking path curve to generate the tracking path curve of the next cycle includes:
Extracting parameters from the tracking path curve to replace the state constant values of the N-1 th group as updated state constant values of the N-1 st group, sequentially taking the state constant values of the s-1 st group as new state constant values of the s-2 nd group, and generating the tracking path curve of the next period based on updating all the state constant values, wherein s is more than or equal to 3 and less than or equal to N.
8. A path prediction method apparatus based on a visual navigation vehicle, comprising:
the coordinate system construction module is configured to construct an orbit equation coordinate system by taking the vehicle-mounted camera as an origin, wherein the positive direction of the x-axis of the coordinate system is perpendicular to the headstock and points to the running direction of the vehicle, and the positive direction of the y-axis of the coordinate system is parallel to the headstock and points to the global reference orbit direction;
the equation construction module is configured to enable the vehicle to run along the global reference track, acquire local reference track information for multiple times by the camera within the range that the running distance is the maximum perceived longitudinal distance of the camera, and construct a plurality of track line equations based on the local reference track information acquired multiple times;
The mapping module is configured to determine a tracking path starting point based on the track equation coordinate system where the track line equation corresponding to the local reference track information acquired last time is located, and a plurality of groups of state constant values are obtained by carrying out translation and rotation mapping on a plurality of track line equations;
The terminal point estimating module is configured to estimate the terminal point of the tracking path through calculation based on the track line equation corresponding to the local reference track information acquired last time and a plurality of track line equations subjected to translation and rotation mapping, so as to obtain a plurality of estimated terminal points;
the end point determining module is configured to obtain a tracking path end point through calculation based on a plurality of estimated end points;
A path generation module configured to determine a tracking path curve based on the tracking path start point and the tracking path end point;
and the iteration updating module is configured to carry out iteration updating on a plurality of groups of state constant values based on the tracking path curve so as to generate the tracking path curve of the next period.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method according to any one of claims 1 to 7 when the computer program is executed.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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Non-Patent Citations (1)
| Title |
|---|
| 面向低速自动驾驶车辆的避障规划研究;肖宏宇;付志强;陈慧勇;;同济大学学报(自然科学版);20191215(第S1期);全文 * |
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