CN112363511A - Vehicle path planning method and device, vehicle-mounted device and storage medium - Google Patents
Vehicle path planning method and device, vehicle-mounted device and storage medium Download PDFInfo
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Abstract
The application provides a vehicle path planning method, which comprises the following steps: constructing an environment map for path planning; determining a learning route according to the environment map, wherein the learning route comprises a starting point, an end point and a plurality of path points arranged between the starting point and the end point; acquiring obstacle information on the learning route in real time; updating the environment map according to the obstacle information to obtain a fusion map; judging whether the plurality of path points meet the anti-collision requirement or not according to the fusion map; if not, updating the position information of the path points until all the path points meet the anti-collision requirement; acquiring the current position of the vehicle and taking the current position as a starting point; and updating the learning route according to the end point, the updated starting point and the updated path point. The application also provides a vehicle path planning device, a vehicle-mounted device and a storage medium. The learning route can be updated in real time according to the barrier information, so that the route planning is realized.
Description
Technical Field
The application relates to the technical field of vehicle path planning, in particular to a path planning method, a path planning device, a vehicle-mounted device and a storage medium.
Background
In recent years, with the continuous development and application of automatic driving technology, the comfort and rationality of automatic driving become more and more important. As a key technology in automatic driving: vehicle path planning attracts more and more people's attention.
The vehicle path planning can plan a route according to the departure point and the destination of the user, however, in the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: the existing point-to-point path planning technology based on a fixed learning route can only adopt an emergency braking and stopping mode to avoid obstacles appearing on the learning route, and cannot meet the requirement of point-to-point path planning in a complex environment.
Disclosure of Invention
In view of the above problems, the present application provides a vehicle path planning method, device, vehicle-mounted device and storage medium to solve the above problems.
A first aspect of the present application provides a vehicle path planning method, the method comprising:
step S1: constructing an environment map for path planning;
step S2: determining a learning route according to the environment map, wherein the learning route comprises a starting point, an end point and a plurality of path points arranged between the starting point and the end point;
step S3: acquiring obstacle information on the learning route in real time;
step S4: updating the environment map according to the obstacle information to obtain a fusion map;
step S5: judging whether the plurality of path points meet the anti-collision requirement or not according to the fusion map;
step S6: if not, updating the position information of the path points which do not meet the anti-collision requirement until all the path points meet the anti-collision requirement;
step S7: acquiring the current position of the vehicle and taking the current position as a starting point;
step S8: and updating the learning route according to the end point, the updated starting point and the updated path point.
Therefore, the learning route is updated in real time through the obstacle information on the learning route, and the path planning is completed.
In some embodiments, the determining whether the plurality of waypoints meet the collision avoidance requirement according to the merged map specifically includes:
step S51: determining a collision area according to preset vehicle information and the position coordinates of the path points;
step S52: judging whether the barrier is positioned in the collision area or not according to the barrier information;
step S53: if so, the path point located in the collision area does not meet the requirement of collision avoidance.
In this way, the position of the path point in the collision area on the fusion map is judged through the vehicle information, and whether the obstacle is located in the collision area is judged so as to judge whether the path point meets the anti-collision requirement.
In some embodiments, the obtaining obstacle information on the learning route in real time specifically includes:
step S31: receiving an environmental image of the vehicle;
step S32: identifying a target object in the environmental image;
step S33: and acquiring the position information of the target object.
In this way, the information of the obstacle is acquired from the environment image of the vehicle.
In some embodiments, the method further comprises:
step S9: receiving a first environment image of the vehicle at a first time point;
step S10: receiving a second environment image of the vehicle at a second point in time, wherein the first point in time is prior to the second point in time, the first environment image and the second environment image each including the target object;
step S11: acquiring first position information of a target object in the first environment image and second position information of the target object in the second environment image;
step S12: acquiring the motion state of the target object according to the first time point, the second time point, the first position information and the second position information;
step S13: acquiring the current motion state of the vehicle;
step S14: and judging whether the path points meet the collision requirement or not according to the current motion state of the vehicle and the motion state of the target object.
Therefore, the motion state of the obstacle is obtained through the environment image, and whether the multiple path points meet the collision requirement or not is judged according to the current motion state of the vehicle and the motion state of the target object.
In some embodiments, the constructing an environment map for path planning specifically includes:
and constructing a grid map for path planning according to the Bayesian principle.
Thus, the grid map can be quickly constructed and updated through the Bayesian principle.
A second aspect of the present application provides a vehicle path planning apparatus, the apparatus comprising:
the building module is used for building an environment map for path planning;
the determining module is used for determining a learning route according to the environment image, wherein the learning route comprises a starting point, an end point and a plurality of path points arranged between the starting point and the end point;
the acquisition module is used for acquiring barrier information on the learning route in real time;
the updating module is used for updating the environment map according to the obstacle information so as to obtain a fusion map;
the judging module is used for judging whether the path points meet the anti-collision requirement or not according to the fusion map;
if not, the updating module is further configured to update the position information of the waypoints that do not meet the anti-collision requirement until all the waypoints meet the anti-collision requirement;
the acquisition module is further used for acquiring the current position of the vehicle and taking the current position as a starting point; the updating module is also used for updating the learning route according to the end point, the updated starting point and the updated path point.
In some embodiments, the determining module comprises:
the sub-determination module is used for determining a collision area according to preset vehicle information and the position coordinates of the path points;
and the sub-judgment module is used for judging whether the barrier is positioned in the collision area or not according to the barrier information, and if the barrier is positioned in the collision area, the sub-judgment module judges that the path point positioned in the collision area does not meet the anti-collision requirement.
In some embodiments, the obtaining module comprises:
the sub-receiving module is used for receiving the environment image of the vehicle;
the sub-recognition module is used for recognizing a target object of the environment image;
and the sub-acquisition module is used for acquiring the position information of the target object.
A third aspect of the present application provides an in-vehicle apparatus including:
the device comprises a memory, a processor and a communication bus, wherein the memory is in communication connection with the processor through the communication bus; and a plurality of program modules stored in the memory, the program modules being loaded by the processor and executing the vehicle path planning method as described above.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a vehicle path planning method as described above.
According to the vehicle path planning method, the fusion map is obtained by updating the barrier information obtained in real time to the environment map, whether path points in the fusion map meet collision requirements or not is judged, the path points all meet the collision requirements by updating the positions of the path points, and the learning route is updated by the updated path points, so that vehicle path planning is achieved, the updating speed is high, and the vehicle path planning method is suitable for scenes such as intelligent driving.
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Fig. 1 is a schematic flow chart of a vehicle path planning method according to an embodiment of the present application.
Fig. 2 is a functional block diagram of a vehicle path planning apparatus according to an embodiment of the present disclosure.
Fig. 3 is a schematic structural diagram of an in-vehicle device according to an embodiment of the present application.
Detailed Description
In order that the objects, features and advantages of the present application can be more clearly understood, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict. In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and the described embodiments are merely a subset of the embodiments of the present application and are not intended to be a complete embodiment.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a vehicle path planning method according to an embodiment of the present application. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs. For convenience of explanation, only portions related to the embodiments of the present application are shown.
The vehicle path planning method is applied to the vehicle. For the vehicle needing path planning, the vehicle path planning function provided by the method of the present application can be directly integrated on the vehicle, or a client for implementing the vehicle path planning method of the present application is installed. For another example, the vehicle path planning method provided by the present application may further be operated on the vehicle in a form of a Software Development Kit (SDK), an interface of the vehicle path planning function is provided in a form of the SDK, and the processor or other device may implement the vehicle path planning function through the provided interface. The vehicle path planning method comprises the following steps.
And step S1, constructing an environment map for path planning.
In one embodiment, the environment map for path planning is a map of a target area, such as a vehicle training field.
In an embodiment, step S1 specifically includes:
and constructing a grid map for path planning according to the Bayesian principle.
The grid map can be quickly constructed according to the Bayesian principle, and the grid map can be updated according to the environmental parameters.
And step S2, determining a learning route according to the environment map.
Wherein the learned route includes a start point, an end point, and a plurality of waypoints disposed between the start point and the end point. It is understood that the number of waypoints may be determined according to the distance between the starting point and the ending point, or according to the actual environment of the learned route, for example, the terrain of the road segment is complex, and the arrangement density of waypoints needs to be increased.
In one embodiment, the learned route may be a driving exam-specific route or the like.
And step S3, acquiring the obstacle information on the learning route in real time.
In an embodiment, step S3 specifically includes:
receiving an environmental image of the vehicle;
identifying a target object in the environmental image;
and acquiring the position information of the target object.
Specifically, the position information of the target object is obtained by receiving an environment image shot by a camera module arranged on the vehicle and identifying the target object in the environment image, wherein the position information is a position coordinate of the target object in the fusion map, and it can be understood that the position information is area information, the area information is an area where the target object is located, and the position coordinate can be a two-dimensional coordinate or a three-dimensional coordinate value.
And step S4, updating the environment map according to the obstacle information to acquire a fusion map.
Specifically, the obstacle information is supplemented into the environment map to acquire the fusion map.
It will be appreciated that in one embodiment the obstacle is a fixed object, such as a car parked in the center of the road.
In one embodiment, the environment map is updated according to Bayesian principles.
And step S5, judging whether the path points meet the anti-collision requirement according to the fusion map.
In an embodiment, step S5 specifically includes:
determining a collision area according to preset vehicle information and the position coordinates of the path points;
judging whether the barrier is positioned in the collision area or not according to the barrier information;
if so, the path point positioned in the collision area does not meet the anti-collision requirement;
if not, the path point meets the anti-collision requirement.
Specifically, a collision area in the learned route is determined according to the size of the vehicle and the route points, wherein the collision area is an area occupied by the vehicle when the vehicle drives the learned route, and if the area where the obstacle is located is overlapped with the areas occupied by the vehicle when the vehicle drives at some of the route points, the obstacle is determined to be located in the collision area of the vehicle, and the route point is further determined not to meet the anti-collision requirement.
If so, the process goes to step S3.
If not, go to step S6: and updating the position information of the path points which do not meet the collision requirement until all the path points meet the collision-prevention requirement.
In an embodiment, the position information of the waypoints not meeting the collision requirement is updated until all of the waypoints meet the collision avoidance requirement.
Specifically, in an embodiment, step S6 specifically includes:
determining a collision area on the learning route according to the obstacle information;
and determining position information of a path point in the collision area according to preset vehicle information so that a safety area of the path is not overlapped with the collision area.
Further, if it is determined that the number of the position information of the waypoints in the collision area is multiple according to the preset vehicle information, the method further includes:
and determining the position information of the path point to be updated according to the path point adjacent to the collision area.
Specifically, the position information of the waypoint to be updated closest to the adjacent waypoint of the collision region may be selected.
And step S7, acquiring the current position of the vehicle and updating the current position as a starting point.
Specifically, the specific position information of the vehicle within the fusion map is made to learn the start point of the route more.
And step S8, updating the learning route according to the end point, the updated starting point and the updated path point.
Therefore, the learning route on the fusion map is updated in real time by acquiring the obstacle information on the learning route in real time, so that the running vehicle can quickly avoid obstacles and quickly return to the learning route.
It is understood that in other embodiments, the obstacle is a moving object, such as a moving vehicle, the method further comprises:
receiving a first environment image of the vehicle at a first time point;
receiving a second environment image of the vehicle at a second point in time, wherein the first point in time is prior to the second point in time, the first environment image and the second environment image each including the target object;
acquiring first position information of the target object in the first environment image and second position information of the target object in the second environment image;
acquiring the motion state of the target object according to the first time point, the second time point, the first position information and the second position information;
acquiring the current motion state of the vehicle;
and judging whether the path points meet the collision requirement or not according to the current motion state of the vehicle and the motion state of the target object.
Specifically, the environment image containing the target object is respectively acquired at the first time and the second time to acquire the motion state of the target object, and whether the path point on the learning route meets the collision requirement or not can be judged according to the motion state of the vehicle amount.
For example, if the speed of the obstacle is 4m/s and the traveling speed of the vehicle is 20m/s, and if the path point near the meeting point is located in the collision region of the obstacle, the meeting point of the vehicle and the obstacle is determined.
Fig. 1 describes the vehicle path planning method in detail, and by the method, the learning route can be updated in real time according to the barrier information acquired in real time, so that the vehicle path planning can be completed quickly. The functional modules and the hardware device architecture for implementing the vehicle path planning device are described below with reference to fig. 2 and 3.
Fig. 2 is a functional block diagram of a vehicle path planning apparatus according to an embodiment of the present disclosure.
In some embodiments, the vehicle path planning apparatus 100 may include a plurality of functional modules composed of program code segments. The program codes of the respective program segments in the vehicle path planning apparatus 100 may be stored in the memory of the in-vehicle apparatus 10 and executed by at least one processor in the in-vehicle apparatus 10 to implement the function of horizontal autonomous parking.
Referring to fig. 2, in the present embodiment, the vehicle path planning apparatus 100 may be divided into a plurality of functional modules according to the functions performed by the apparatus, and each functional module is configured to perform each step in the corresponding embodiment of fig. 1 to implement the function of vehicle path planning. In this embodiment, the functional modules of the vehicle route planning apparatus 100 include: the device comprises a construction module 101, a determination module 102, an acquisition module 103, an update module 104 and a judgment module 105.
The building module 101 is configured to build an environment map for path planning.
The determining module 102 is configured to determine a learned route according to the environment map, where the learned route includes a starting point, an end point, and a plurality of waypoints disposed between the starting point and the end point.
The obtaining module 103 is configured to obtain obstacle information on the learning route in real time.
Further, the obtaining module 103 constructs a grid map for path planning according to a bayesian principle.
The updating module 104 is configured to update the environment map according to the obstacle information to obtain a fusion map.
The judging module 105 judges whether the plurality of waypoints meet the anti-collision requirement according to the fusion map;
if not, the updating module 104 is further configured to update the position information of the waypoints that do not meet the collision requirement until all the waypoints meet the collision-prevention requirement;
the obtaining module 103 is further configured to obtain a current position of the vehicle and use the current position as a starting point;
the updating module 104 is further configured to update the learning route according to the end point, the updated start point, and the updated route point.
The determining module 105 includes:
the sub-determination module is used for determining a collision area according to preset vehicle information and the position coordinates of the path points;
and the sub-judgment module is used for judging whether the barrier is positioned in the collision area or not according to the barrier information.
Further, the obtaining module 103 includes:
the sub-receiving module is used for receiving the environment image of the vehicle;
the sub-recognition module is used for recognizing a target object in the environment image;
and the sub-acquisition module is used for acquiring the position information of the target object.
The vehicle path planning apparatus 100 further includes:
a receiving module 106 for receiving a first environment image of the vehicle at a first time; for receiving a second environmental image of the vehicle at a second time, wherein the first time is prior to the second time, the first environmental image and the second environmental image each including the target object.
The obtaining module 103 is further configured to obtain first position information of the target object in the first environment image and second position information of the target object in the second environment image; the obtaining module 103 obtains the motion state of the target object according to the first time, the second time, the first position information and the second position information; the acquisition module 103 acquires a current motion state of the vehicle.
The determining module 105 determines whether the plurality of waypoints meet the collision requirement according to the current motion state of the vehicle and the motion state of the target object.
Fig. 3 is a schematic structural diagram of an in-vehicle device according to an embodiment of the present application. The vehicle-mounted device 10 comprises a memory 11, a processor 12 and a communication bus 13, wherein the memory 11 is connected with the processor 12 in a communication mode through the communication bus 13.
The on-board device 10 further includes a computer program 14, such as a program for vertical autonomous parking control, stored in the memory 11 and executable on the processor 12.
The steps of the vehicle path planning method in the method embodiment are implemented when the computer program 14 is executed by the processor 12. Alternatively, the processor 12 executes the computer program 14 to realize the functions of the modules/units in the system embodiment.
The computer program 14 may be partitioned into one or more modules/units that are stored in the memory 11 and executed by the processor 12 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, the instruction segments describing the execution process of the computer program 14 in the electronic device 1. For example, the computer program 14 may be partitioned into modules 101 and 106 in FIG. 3.
It will be understood by those skilled in the art that the schematic diagram 3 is merely an example of the in-vehicle apparatus 10, and does not constitute a limitation to the in-vehicle apparatus 10, and the in-vehicle apparatus 10 may include more or less components than those shown, or combine some components, or different components, for example, the in-vehicle apparatus 10 may further include an input device, etc.
The Processor 12 may be a Central Processing Unit (CPU), and may include other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 12 is a control center of the in-vehicle apparatus 10 and connects various parts of the entire in-vehicle apparatus 10 by various interfaces and lines.
The memory 11 may be used for storing the computer program 14 and/or the modules/units, and the processor 12 may implement various functions of the in-vehicle apparatus 10 by running or executing the computer program and/or the modules/units stored in the memory 11 and calling data stored in the memory 11. The storage 11 may include an external storage medium and may also include a memory. In addition, the memory 11 may include a high speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The modules/units integrated with the in-vehicle apparatus 10 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the processes in the methods of the embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the steps of the embodiments of the methods. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.
Claims (10)
1. A vehicle path planning method, characterized in that the method comprises:
step S1: constructing an environment map for path planning;
step S2: determining a learning route according to the environment map, wherein the learning route comprises a starting point, an end point and a plurality of path points arranged between the starting point and the end point;
step S3: acquiring obstacle information on the learning route in real time;
step S4: updating the environment map according to the obstacle information to obtain a fusion map;
step S5: judging whether the plurality of path points meet the anti-collision requirement or not according to the fusion map;
step S6: if not, updating the position information of the path points which do not meet the anti-collision requirement until all the path points meet the anti-collision requirement;
step S7: acquiring the current position of the vehicle and taking the current position as a starting point;
step S8: and updating the learning route according to the end point, the updated starting point and the updated path point.
2. The vehicle path planning method according to claim 1, wherein the determining whether the plurality of waypoints satisfy the collision avoidance requirement according to the merged map specifically includes:
step S51: determining a collision area according to preset vehicle information and the position coordinates of the path points;
step S52: judging whether the barrier is positioned in the collision area or not according to the barrier information;
step S53: if so, the path point located in the collision area does not meet the requirement of collision avoidance.
3. The vehicle path planning method according to claim 1 or 2, wherein the obtaining of the obstacle information on the learned route in real time specifically includes:
step S31: receiving an environmental image of the vehicle;
step S32: identifying a target object in the environmental image;
step S33: and acquiring the position information of the target object.
4. The vehicle path planning method of claim 3, further comprising:
step S9: receiving a first environment image of the vehicle at a first point in time;
step S10: receiving a second environment image of the vehicle at a second point in time, wherein the first point in time is prior to the second point in time, the first environment image and the second environment image each including the target object;
step S11: acquiring first position information of a target object in the first environment image and second position information of the target object in the second environment image;
step S12: acquiring the motion state of the target object according to the first time point, the second time point, the first position information and the second position information;
step S13: acquiring the current motion state of the vehicle;
step S14: and judging whether the path points meet the collision requirement or not according to the current motion state of the vehicle and the motion state of the target object.
5. The vehicle path planning method according to claim 1 or 2, wherein the constructing of the environment map for path planning specifically includes:
and constructing a grid map for path planning according to the Bayesian principle.
6. A vehicle path planning apparatus, the apparatus comprising:
the building module is used for building an environment map for path planning;
the determining module is used for determining a learning route according to the environment image, wherein the learning route comprises a starting point, an end point and a plurality of path points arranged between the starting point and the end point;
the acquisition module is used for acquiring barrier information on the learning route in real time;
the updating module is used for updating the environment map according to the obstacle information so as to obtain a fusion map;
the judging module is used for judging whether the path points meet the anti-collision requirement or not according to the fusion map;
if the path point does not meet the anti-collision requirement, the updating module is further configured to update the position information of the path point that does not meet the anti-collision requirement until all the path points meet the anti-collision requirement;
the obtaining module is further configured to obtain a current position of the vehicle and use the current position as a starting point, and the updating module is further configured to update the learned route according to the end point, the updated starting point and the updated route point.
7. The vehicle path planner of claim 6 wherein the determination module comprises:
the sub-determination module is used for determining a collision area according to preset vehicle information and the position coordinates of the path points;
and the sub-judgment module is used for judging whether the barrier is positioned in the collision area or not according to the barrier information, and if the barrier is positioned in the collision area, the sub-judgment module judges that the path point positioned in the collision area does not meet the anti-collision requirement.
8. The vehicle path planning apparatus according to claim 6 or 7, wherein the acquisition module includes:
the sub-receiving module is used for receiving the environment image of the vehicle;
the sub-recognition module is used for recognizing a target object of the environment image;
and the sub-acquisition module is used for acquiring the position information of the target object.
9. An in-vehicle apparatus characterized by comprising:
the device comprises a memory, a processor and a communication bus, wherein the memory is in communication connection with the processor through the communication bus; and
the memory has stored therein a plurality of program modules that are loaded by the processor and execute the vehicle path planning method according to any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a vehicle path planning method according to any one of claims 1 to 5.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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