CN111397611B - Path planning method and device and electronic equipment - Google Patents
Path planning method and device and electronic equipment Download PDFInfo
- Publication number
- CN111397611B CN111397611B CN202010149254.5A CN202010149254A CN111397611B CN 111397611 B CN111397611 B CN 111397611B CN 202010149254 A CN202010149254 A CN 202010149254A CN 111397611 B CN111397611 B CN 111397611B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- determining
- path
- path planning
- obstacle information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000005070 sampling Methods 0.000 claims description 29
- 230000014509 gene expression Effects 0.000 claims description 24
- 230000015654 memory Effects 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 8
- 230000008878 coupling Effects 0.000 abstract description 7
- 238000010168 coupling process Methods 0.000 abstract description 7
- 238000005859 coupling reaction Methods 0.000 abstract description 7
- 238000012423 maintenance Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 10
- 230000008859 change Effects 0.000 description 5
- 238000012549 training Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 101150036841 minJ gene Proteins 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001151 other effect Effects 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The application discloses a path planning method, a path planning device and electronic equipment, and relates to the technical field of automatic driving. The specific implementation scheme is as follows: acquiring a driving scene and position information of a vehicle; determining obstacle information around the vehicle based on the position information and the map data; determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle; determining a planning feasible region of the vehicle according to the obstacle information and the driving scene of the periphery of the vehicle, which are related to the path planning; the planning method is characterized in that a planning feasible region and a preset path planning algorithm are combined to determine a planning path corresponding to the vehicle, so that the plurality of scene rules are divided into three steps to be limited, the boundary between the steps is clear, the coupling between the plurality of scene rules is reduced, the maintenance difficulty is small, details such as driving scenes are considered, and the accuracy of path planning is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to the field of automatic driving technologies, and in particular, to a path planning method and apparatus, and an electronic device.
Background
The current path planning method is to represent the path planning as a polynomial expression for a specific driving scenario, such as lane change, and determine the planned path by solving the expression.
In the method, the scene rules are coupled together through the polynomial expressions aiming at a specific driving scene, and when the driving scene changes, such as more obstacles, the polynomial expressions are difficult to modify and difficult to maintain. And the polynomial expression considers less details, resulting in low accuracy of path planning.
Disclosure of Invention
The application provides a path planning method, a path planning device and electronic equipment, wherein a driving scene of a vehicle and obstacle information around the vehicle and related to path planning are obtained; determining a planning feasible region of the vehicle according to the obstacle information and the driving scene; the planning method is characterized in that a planning feasible region and a preset path planning algorithm are combined to determine a planning path corresponding to the vehicle, so that the plurality of scene rules are divided into three steps to be limited, the boundary between the steps is clear, the coupling between the plurality of scene rules is reduced, the maintenance difficulty is small, details such as driving scenes are considered, and the accuracy of path planning is improved.
An embodiment of a first aspect of the present application provides a path planning method, including:
acquiring a driving scene and position information of a vehicle;
determining obstacle information around the vehicle according to the position information and the map data;
determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle;
determining a planning feasible region of the vehicle according to the obstacle information around the vehicle and related to the path planning and the driving scene;
and determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm.
In an embodiment of the present application, the determining obstacle information around the vehicle related to route planning according to the obstacle information around the vehicle and the driving scenario of the vehicle includes:
inputting the obstacle information in front of the vehicle, the driving scene and the map data into a preset front obstacle filtering model, and obtaining the correlation degree between the obstacle information in front of the vehicle and the path planning;
inputting the obstacle information behind the vehicle, the driving scene and the map data into a preset rear safety judgment model, and obtaining the correlation degree between the obstacle information behind the vehicle and the path planning;
and determining the obstacle information related to the path planning around the vehicle according to the correlation.
In an embodiment of the present application, the determining a planned feasible region of the vehicle according to the obstacle information around the vehicle and related to the path planning and the driving scenario includes:
determining a road area of the vehicle according to the scene strategy of the driving scene and the map data;
determining a drivable area of the vehicle according to the road area and the obstacle information around the vehicle and related to the path planning;
and determining a planned feasible region of the vehicle according to the feasible region and a vehicle model of the vehicle.
In one embodiment of the present application, the information indicating the planned feasible region of the vehicle includes: the reference line in the planning feasible region and the normal distance range corresponding to each point on the reference line.
In an embodiment of the present application, the determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm includes:
obtaining representation information of the planned path, the representation information including: position information of each sampling point on the planned path; the location information includes: the arc length of a vertical point of the sampling point on the reference line from an initial point of the reference line and the distance information between the sampling point and the vertical point;
constructing a polynomial expression according to the representation information of the planned path and the representation information of the planned feasible domain, wherein the polynomial expression comprises: a path evaluation function and a limiting condition of the sampling point;
and solving the polynomial expression, and determining the path with the minimum corresponding path evaluation value as the planned path corresponding to the vehicle.
According to the path planning method, the driving scene and the position information of the vehicle are obtained; determining obstacle information around the vehicle based on the position information and the map data; determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle; determining a planning feasible region of the vehicle according to the obstacle information and the driving scene of the periphery of the vehicle, which are related to the path planning; and determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm. Therefore, the multiple scene rules are divided into three steps to be limited, the boundary among the steps is clear, the coupling among the multiple scene rules is reduced, the maintenance difficulty is low, details such as driving scenes are considered, and the accuracy of path planning is improved.
An embodiment of a second aspect of the present application provides a path planning apparatus, including:
the acquisition module is used for acquiring the driving scene and the position information of the vehicle;
the first determining module is used for determining obstacle information around the vehicle according to the position information and the map data;
the second determination module is used for determining the obstacle information around the vehicle related to the path planning according to the obstacle information around the vehicle and the driving scene of the vehicle;
the third determination module is used for determining a planning feasible region of the vehicle according to the obstacle information around the vehicle and related to the path planning and the driving scene;
and the fourth determining module is used for determining the planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm.
In an embodiment of the present application, the second determining module is specifically configured to,
inputting the obstacle information in front of the vehicle, the driving scene and the map data into a preset front obstacle filtering model, and obtaining the correlation degree between the obstacle information in front of the vehicle and the path planning;
inputting the obstacle information behind the vehicle, the driving scene and the map data into a preset rear safety judgment model, and obtaining the correlation degree between the obstacle information behind the vehicle and the path planning;
and determining the obstacle information related to the path planning around the vehicle according to the correlation.
In an embodiment of the present application, the third determining module is specifically configured to,
determining a road area of the vehicle according to the scene strategy of the driving scene and the map data;
determining a drivable area of the vehicle according to the road area and the obstacle information around the vehicle and related to the path planning;
and determining a planned feasible region of the vehicle according to the feasible region and a vehicle model of the vehicle.
In one embodiment of the present application, the information indicating the planned feasible region of the vehicle includes: the reference line in the planning feasible region and the normal distance range corresponding to each point on the reference line.
In an embodiment of the present application, the fourth determining module is specifically configured to,
obtaining representation information of the planned path, the representation information including: position information of each sampling point on the planned path; the location information includes: the arc length of a vertical point of the sampling point on the reference line from an initial point of the reference line and the distance information between the sampling point and the vertical point;
constructing a polynomial expression according to the representation information of the planned path and the representation information of the planned feasible domain, wherein the polynomial expression comprises: a path evaluation function and a limiting condition of the sampling point;
and solving the polynomial expression, and determining the path with the minimum corresponding path evaluation value as the planned path corresponding to the vehicle.
The path planning device of the embodiment of the application acquires the driving scene and the position information of the vehicle; determining obstacle information around the vehicle based on the position information and the map data; determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle; determining a planning feasible region of the vehicle according to the obstacle information and the driving scene of the periphery of the vehicle, which are related to the path planning; and determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm. Therefore, the multiple scene rules are divided into three steps to be limited, the boundary among the steps is clear, the coupling among the multiple scene rules is reduced, the maintenance difficulty is low, details such as driving scenes are considered, and the accuracy of path planning is improved.
An embodiment of a third aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a path planning method as described above.
A fourth aspect of the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a path planning method as described above.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present application;
FIG. 2 is a schematic diagram according to a second embodiment of the present application;
FIG. 3 is a schematic view of a road area of a vehicle passing in straight down for overtaking;
FIG. 4 is a schematic view of a road area of a vehicle during a side-by-side stop on a lane change;
FIG. 5 is a schematic view of a travelable area corresponding to the road area in FIG. 3;
FIG. 6 is a schematic view of a travelable area corresponding to the road area in FIG. 4;
FIG. 7 is a schematic illustration according to a third embodiment of the present application;
fig. 8 is a block diagram of an electronic device for implementing a path planning method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The following describes a path planning method, a path planning device, and an electronic device according to an embodiment of the present application with reference to the drawings.
Fig. 1 is a schematic diagram according to a first embodiment of the present application. It should be noted that the main execution body of the path planning method provided in this embodiment is a path planning device. The path planning device may be a hardware device such as a terminal device and a server, or software installed on the hardware device.
As shown in fig. 1, the path planning method is implemented as follows:
In the embodiment of the application, the driving scene of the vehicle is, for example, straight-going side-by-side parking, lane-borrowing and overtaking, bifurcation and convergence and the like; stopping at the side under the lane change, blocking the lane change, converging traffic flow and the like.
In the present embodiment, the obstacle is, for example, a bicycle, a roadside stop vehicle, a running vehicle, a pedestrian, a roadside apparatus, or the like. The map data may be map data of an area where the vehicle is currently located, for example, map data of a lane where the vehicle is located. The map data is inquired according to the position information of the vehicle, and the obstacle information on the lane in a certain range of the position of the vehicle can be obtained.
In the embodiment of the application, the obstacle information around the vehicle related to the path planning refers to obstacles influencing the vehicle to run in the path planning process. The route planning device determines the obstacle information around the vehicle and related to the route planning, for example, the obstacle information, the driving scene and the map data in front of the vehicle are input into a preset front obstacle filtering model, and the correlation degree between the obstacle information in front of the vehicle and the route planning is obtained; inputting the obstacle information, the driving scene and the map data behind the vehicle into a preset rear safety judgment model, and obtaining the correlation degree between the obstacle information behind the vehicle and the path planning; and determining obstacle information related to the path planning around the vehicle according to the correlation.
In this embodiment, the preset front obstacle filtering model may be a model obtained by training an initial front obstacle filtering model using related training data. Wherein the training data may include: a plurality of sample data; each sample data includes: obstacle information, driving scenes, map data, and correlation. In this embodiment, the preset rear safety discrimination model may be a model obtained by training an initial rear safety discrimination model using related training data.
In this embodiment, the obstacle information related to the path planning around the vehicle is acquired, and the path planning is performed by combining the obstacle information, so that the influence of the non-related obstacle information on the path planning can be avoided, for example, the influence on the path planning caused by the fact that the vehicle cannot pass through which part of the lane is limited, and the accuracy of the planned path can be further improved.
And step 104, determining a planning feasible region of the vehicle according to the obstacle information and the driving scene, which are around the vehicle and related to the path planning.
In the embodiment of the application, the area where the vehicle can run on the lane can be determined according to the obstacle information and the driving scene, which are around the vehicle and related to the path planning, and then the area where the center of mass of the vehicle can pass is determined by combining the vehicle model of the vehicle, so that the planning feasible area of the vehicle is generated.
And 105, determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm.
In the embodiment of the present application, the representation information of the planned feasible region of the vehicle includes: and planning a reference line in the feasible region and a normal distance range corresponding to each point on the reference line. Correspondingly, the process of the path planning apparatus executing step 105 may specifically be that the representation information of the planned path is acquired, and the representation information includes: planning the position information of each sampling point on the path; the location information includes: the arc length of a vertical point of the sampling point on the reference line from an initial point of the reference line and the distance information between the sampling point and the vertical point; constructing a polynomial expression according to the representation information of the planned path and the representation information of the planned feasible domain, wherein the polynomial expression comprises: a path evaluation function and a limiting condition of a sampling point; and solving the polynomial expression, and determining the path with the minimum corresponding path evaluation value as a planned path corresponding to the vehicle.
In this embodiment, reference lines in the planned feasible region may be, for example, lane center lines, lane boundary lines, and the like. The lane center line is parallel to the lane boundary line, and the distances from the respective points on the lane center line to the lane boundary line are generally the same. In this embodiment, the initial point of the reference line is determined according to the position information of the vehicle, and a vertical point obtained by projecting the position of the vehicle onto the reference line is the initial point of the reference line.
In this embodiment, the reference line may be represented as s, and the position information of each sampling point on the planned path may be represented as(s)0，l0)，(s1，l1)，…，(sn，ln) And the like. Wherein s isnThe arc length of the vertical point of the nth sampling point on the reference line from the initial point of the reference line is represented; lnRepresenting distance information between the sampling point and the vertical point. The polynomial expression may be, for example, as shown in the following formula (1), formula (2), and formula (3).
minJ(l0,l1,...ln) (1)
lub_i≥li≥llb_i,i＝0,1,...,n (2)
Wherein lub_iRepresenting the maximum value of the normal distance range corresponding to the vertical point of the ith sampling point on the reference line; llb_iAnd the minimum value of the normal distance range corresponding to the vertical point of the ith sampling point on the reference line is shown.
According to the path planning method, the driving scene and the position information of the vehicle are obtained; determining obstacle information around the vehicle based on the position information and the map data; determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle; determining a planning feasible region of the vehicle according to the obstacle information and the driving scene of the periphery of the vehicle, which are related to the path planning; and determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm. Therefore, the multiple scene rules are divided into three steps to be limited, the boundary among the steps is clear, the coupling among the multiple scene rules is reduced, the maintenance difficulty is low, details such as driving scenes are considered, and the accuracy of path planning is improved.
Fig. 2 is a schematic diagram according to a second embodiment of the present application. On the basis of the embodiment shown in fig. 1, step 104 may specifically include the following steps:
In the embodiment of the application, the path planning device can determine the lane boundary and the road boundary of the lane where the vehicle is located by combining the map data and the position information of the vehicle; wherein the lane boundary is a lane line of a lane where the vehicle is located; a road boundary is the boundary of a drivable area of the entire road surface, such as a road shoulder, a fence, etc. And determining a plane boundary by combining the lane boundary of the lane where the vehicle is located, the road boundary and a scene strategy of a driving scene, and determining a road area of the vehicle according to the plane boundary. The map data may be map data of a high-precision map.
When the driving scene is a straight-driving lane-borrowing overtaking, the schematic diagram of the road area of the vehicle can be shown in fig. 3. In fig. 3, the blank area in the upper right corner is an area where the vehicle is not allowed to run when the lane-crossing is removed according to the driving scene. In fig. 3, a denotes a vehicle to be subjected to path planning; b represents a front stuck blocked vehicle; and C represents a rear-running bicycle. The shaded portion in fig. 3 represents the road area of the vehicle a.
In the case of parking beside a lane under a lane change, a schematic view of a road area of a vehicle may be as shown in fig. 4. In fig. 4, the blank area in the upper right corner is an area where the vehicle is not allowed to run when the vehicle is parked alongside, which is removed according to the driving scene. In fig. 4, a denotes a vehicle to be subjected to path planning; and B represents a roadside pedestrian. The shaded portion in fig. 4 represents the road area of the vehicle a.
In the embodiment of the application, the safe distance which needs to be kept by various types of obstacles is determined according to the obstacle information, the road area is adjusted according to the safe distance, and the area related to the safe distance in the road area is removed. Fig. 5 is a schematic diagram of a travelable area corresponding to the road area in fig. 3. In fig. 5, the upper unshaded horizontal bar portion is a region related to the safety distance of C.
Fig. 6 is a schematic diagram of a travelable area corresponding to the road area in fig. 4. In fig. 6, the unshaded horizontal bar portion above B is a region related to the safety distance of B.
In the embodiment of the present application, the vehicle model may be, for example, an outline of the vehicle, a kinematic model, or the like. The distance between the center of mass of the vehicle and the outline of the vehicle can be determined according to the outline of the vehicle, and the area related to the distance is deducted from the travelable area. The kinematic model, for example the farthest distance between the center of mass of the vehicle and the outline of the vehicle when the vehicle turns, requires deduction of the area associated with this farthest distance at the lane turn. In the embodiment of the application, the planned feasible region of the vehicle is a region through which the center of mass of the vehicle can pass.
According to the path planning method, the driving scene and the position information of the vehicle are obtained; determining obstacle information around the vehicle based on the position information and the map data; determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle; determining a road area of the vehicle according to a scene strategy of a driving scene and map data; determining a drivable area of the vehicle according to the road area and the obstacle information around the vehicle related to the path planning; determining a planned feasible region of the vehicle according to the feasible region and a vehicle model of the vehicle; and determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm. Therefore, the multiple scene rules are divided into three steps to be limited, the boundary among the steps is clear, the coupling among the multiple scene rules is reduced, the maintenance difficulty is low, details such as driving scenes are considered, and the accuracy of path planning is improved.
In order to implement the embodiments described in fig. 1 to fig. 2, an embodiment of the present application further provides a path planning apparatus.
Fig. 7 is a schematic diagram according to a third embodiment of the present application. As shown in fig. 7, the path planning apparatus 700 includes: an acquisition module 710, a first determination module 720, a second determination module 730, a third determination module 740, and a fourth determination module 750.
The acquiring module 710 is configured to acquire a driving scene and position information of a vehicle;
a first determining module 720, configured to determine obstacle information around the vehicle according to the position information and the map data;
the second determining module 730 is configured to determine obstacle information around the vehicle, which is related to a path plan, according to the obstacle information around the vehicle and the driving scene of the vehicle;
a third determining module 740, configured to determine a planned feasible region of the vehicle according to the obstacle information around the vehicle and related to the path planning and the driving scenario;
and a fourth determining module 750, configured to determine, by combining the planned feasible region and a preset path planning algorithm, a planned path corresponding to the vehicle.
In this embodiment of the application, the second determining module 730 is specifically configured to input the obstacle information in front of the vehicle, the driving scene, and the map data into a preset front obstacle filtering model, and obtain a correlation between the obstacle information in front of the vehicle and a path plan; inputting the obstacle information behind the vehicle, the driving scene and the map data into a preset rear safety judgment model, and obtaining the correlation degree between the obstacle information behind the vehicle and the path planning; and determining the obstacle information related to the path planning around the vehicle according to the correlation.
In this embodiment of the application, the third determining module 740 is specifically configured to determine a road area of the vehicle according to a scene policy of the driving scene and map data; determining a drivable area of the vehicle according to the road area and the obstacle information around the vehicle and related to the path planning; and determining a planned feasible region of the vehicle according to the feasible region and a vehicle model of the vehicle.
In an embodiment of the present application, the information representing the planned feasible region of the vehicle includes: the reference line in the planning feasible region and the normal distance range corresponding to each point on the reference line.
In this embodiment of the application, the fourth determining module 750 is specifically configured to obtain the indicating information of the planned path, where the indicating information includes: position information of each sampling point on the planned path; the location information includes: the arc length of a vertical point of the sampling point on the reference line from an initial point of the reference line and the distance information between the sampling point and the vertical point; constructing a polynomial expression according to the representation information of the planned path and the representation information of the planned feasible domain, wherein the polynomial expression comprises: a path evaluation function and a limiting condition of the sampling point; and solving the polynomial expression, and determining the path with the minimum corresponding path evaluation value as the planned path corresponding to the vehicle.
The path planning device of the embodiment of the application acquires the driving scene and the position information of the vehicle; determining obstacle information around the vehicle based on the position information and the map data; determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle; determining a planning feasible region of the vehicle according to the obstacle information and the driving scene of the periphery of the vehicle, which are related to the path planning; and determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm. Therefore, the multiple scene rules are divided into three steps to be limited, the boundary among the steps is clear, the coupling among the multiple scene rules is reduced, the maintenance difficulty is low, details such as driving scenes are considered, and the accuracy of path planning is improved.
In order to implement the above embodiments, an electronic device is further provided in the embodiments of the present application.
Fig. 8 is a block diagram of an electronic device according to a path planning method in an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 8, the electronic apparatus includes: one or more processors 801, memory 802, and interfaces for connecting the various components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 8 illustrates an example of a processor 801.
The memory 802 is a non-transitory computer readable storage medium as provided herein. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the path planning method provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the path planning method provided by the present application.
The memory 802, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the path planning methods in the embodiments of the present application (e.g., the obtaining module 710, the first determining module 720, the second determining module 730, the third determining module 740, and the fourth determining module 750 shown in fig. 7). The processor 801 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 802, that is, implements the path planning method in the above-described method embodiment.
The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device for path planning, and the like. Further, the memory 802 may include high speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 802 optionally includes memory located remotely from the processor 801, which may be connected to the path planning electronics via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the path planning method may further include: an input device 803 and an output device 804. The processor 801, the memory 802, the input device 803, and the output device 804 may be connected by a bus or other means, and are exemplified by a bus in fig. 8.
The input device 803 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the path-planning electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer, one or more mouse buttons, a track ball, a joystick, or other input device. The output devices 804 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of path planning, comprising:
acquiring a driving scene and position information of a vehicle;
determining obstacle information around the vehicle according to the position information and the map data;
determining obstacle information related to path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle;
determining a planning feasible region of the vehicle according to the obstacle information around the vehicle and related to the path planning and the driving scene;
determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm;
the determining the obstacle information related to the path planning around the vehicle according to the obstacle information around the vehicle and the driving scene of the vehicle includes:
inputting the obstacle information in front of the vehicle, the driving scene and the map data into a preset front obstacle filtering model, and obtaining the correlation degree between the obstacle information in front of the vehicle and the path planning;
inputting the obstacle information behind the vehicle, the driving scene and the map data into a preset rear safety judgment model, and obtaining the correlation degree between the obstacle information behind the vehicle and the path planning;
and determining the obstacle information related to the path planning around the vehicle according to the correlation.
2. The method of claim 1, wherein determining a planned range of the vehicle based on the obstacle information related to the path planning around the vehicle and the driving scenario comprises:
determining a road area of the vehicle according to the scene strategy of the driving scene and the map data;
determining a drivable area of the vehicle according to the road area and the obstacle information around the vehicle and related to the path planning;
and determining a planned feasible region of the vehicle according to the feasible region and a vehicle model of the vehicle.
3. The method of claim 1, wherein the representation information of the planned feasible region of the vehicle comprises: the reference line in the planning feasible region and the normal distance range corresponding to each point on the reference line.
4. The method according to claim 3, wherein the determining the planned path corresponding to the vehicle in combination with the planned feasible region and a preset path planning algorithm comprises:
obtaining representation information of the planned path, the representation information including: position information of each sampling point on the planned path; the location information includes: the arc length of a vertical point of the sampling point on the reference line from an initial point of the reference line and the distance information between the sampling point and the vertical point;
constructing a polynomial expression according to the representation information of the planned path and the representation information of the planned feasible domain, wherein the polynomial expression comprises: a path evaluation function and a limiting condition of the sampling point;
and solving the polynomial expression, and determining the path with the minimum corresponding path evaluation value as the planned path corresponding to the vehicle.
5. A path planning apparatus, comprising:
the acquisition module is used for acquiring the driving scene and the position information of the vehicle;
the first determining module is used for determining obstacle information around the vehicle according to the position information and the map data;
the second determination module is used for determining the obstacle information around the vehicle related to the path planning according to the obstacle information around the vehicle and the driving scene of the vehicle;
the third determination module is used for determining a planning feasible region of the vehicle according to the obstacle information around the vehicle and related to the path planning and the driving scene;
the fourth determining module is used for determining a planned path corresponding to the vehicle by combining the planned feasible region and a preset path planning algorithm;
the second determining means is specifically configured to,
inputting the obstacle information in front of the vehicle, the driving scene and the map data into a preset front obstacle filtering model, and obtaining the correlation degree between the obstacle information in front of the vehicle and the path planning;
inputting the obstacle information behind the vehicle, the driving scene and the map data into a preset rear safety judgment model, and obtaining the correlation degree between the obstacle information behind the vehicle and the path planning;
and determining the obstacle information related to the path planning around the vehicle according to the correlation.
6. The apparatus of claim 5, wherein the third determining means is specifically configured to,
determining a road area of the vehicle according to the scene strategy of the driving scene and the map data;
determining a drivable area of the vehicle according to the road area and the obstacle information around the vehicle and related to the path planning;
and determining a planned feasible region of the vehicle according to the feasible region and a vehicle model of the vehicle.
7. The apparatus of claim 5, wherein the representation information of the planned feasible region of the vehicle comprises: the reference line in the planning feasible region and the normal distance range corresponding to each point on the reference line.
8. The apparatus of claim 7, wherein the fourth determination module is specifically configured to,
obtaining representation information of the planned path, the representation information including: position information of each sampling point on the planned path; the location information includes: the arc length of a vertical point of the sampling point on the reference line from an initial point of the reference line and the distance information between the sampling point and the vertical point;
constructing a polynomial expression according to the representation information of the planned path and the representation information of the planned feasible domain, wherein the polynomial expression comprises: a path evaluation function and a limiting condition of the sampling point;
and solving the polynomial expression, and determining the path with the minimum corresponding path evaluation value as the planned path corresponding to the vehicle.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010149254.5A CN111397611B (en) | 2020-03-05 | 2020-03-05 | Path planning method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010149254.5A CN111397611B (en) | 2020-03-05 | 2020-03-05 | Path planning method and device and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111397611A CN111397611A (en) | 2020-07-10 |
| CN111397611B true CN111397611B (en) | 2022-07-05 |
Family
ID=71436283
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010149254.5A Active CN111397611B (en) | 2020-03-05 | 2020-03-05 | Path planning method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111397611B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112415995B (en) * | 2020-09-22 | 2023-08-01 | 北京智行者科技股份有限公司 | Planning control method based on real-time safety boundary |
| CN113009918B (en) * | 2021-03-09 | 2023-12-05 | 京东鲲鹏(江苏)科技有限公司 | Path planning method, device, system and readable storage medium |
| CN113834497A (en) * | 2021-09-24 | 2021-12-24 | 合众新能源汽车有限公司 | Automatic driving route planning method and device |
| CN114279455B (en) * | 2021-11-16 | 2024-06-21 | 北京百度网讯科技有限公司 | Path planning method, path planning device, electronic equipment and readable storage medium |
| CN114264312A (en) * | 2021-11-30 | 2022-04-01 | 阿波罗智联(北京)科技有限公司 | Path planning method and device for automatic driving vehicle and automatic driving vehicle |
| CN115326100B (en) * | 2022-10-17 | 2023-03-03 | 北京智行者科技股份有限公司 | Path planning method and device, automatic driving equipment and computer storage medium |
| CN118243105A (en) * | 2024-03-28 | 2024-06-25 | 北京小米机器人技术有限公司 | Path planning method, path planning device, electronic equipment and storage medium |
| CN118025235B (en) * | 2024-04-12 | 2024-07-12 | 智道网联科技(北京)有限公司 | Automatic driving scene understanding method, device and system and electronic equipment |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9435654B2 (en) * | 2013-06-01 | 2016-09-06 | Savari, Inc. | System and method for creating, storing, and updating local dynamic MAP database with safety attribute |
| EP3244381B1 (en) * | 2015-01-05 | 2019-03-27 | Nissan Motor Co., Ltd. | Target path generation device and travel control device |
| CN105929823A (en) * | 2016-04-29 | 2016-09-07 | 大连楼兰科技股份有限公司 | Automatic driving system and driving method based on existing map |
| CN107664504A (en) * | 2016-07-29 | 2018-02-06 | 法乐第(北京)网络科技有限公司 | A kind of path planning apparatus |
| CN106846870A (en) * | 2017-02-23 | 2017-06-13 | 重庆邮电大学 | The intelligent parking system and method for the parking lot vehicle collaboration based on centralized vision |
| CN107843267B (en) * | 2017-10-25 | 2020-04-17 | 广州汽车集团股份有限公司 | Path generation method and device for unmanned vehicle in construction road section |
| JP2019139368A (en) * | 2018-02-07 | 2019-08-22 | パイオニア株式会社 | Information display control device, information display control method and information display control program |
| CN108763287B (en) * | 2018-04-13 | 2021-11-12 | 同济大学 | Construction method of large-scale passable regional driving map and unmanned application method thereof |
| CN109828592B (en) * | 2019-04-22 | 2019-07-26 | 深兰人工智能芯片研究院（江苏）有限公司 | A kind of method and apparatus of detection of obstacles |
| CN109976355B (en) * | 2019-04-26 | 2021-12-10 | 腾讯科技（深圳）有限公司 | Trajectory planning method, system, device and storage medium |
| CN110263628B (en) * | 2019-05-09 | 2021-11-23 | 杭州飞步科技有限公司 | Obstacle detection method, obstacle detection device, electronic apparatus, and storage medium |
-
2020
- 2020-03-05 CN CN202010149254.5A patent/CN111397611B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111397611A (en) | 2020-07-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111397611B (en) | Path planning method and device and electronic equipment | |
| CN111595358B (en) | Navigation data processing method, route guidance method, device and storage medium | |
| CN111611901B (en) | Vehicle reverse running detection method, device, equipment and storage medium | |
| EP3798903A1 (en) | Method and apparatus for autonomous driving control, electronic device, and storage medium | |
| CN111967301A (en) | Positioning navigation method, device, electronic equipment and storage medium | |
| CN110675644B (en) | Method and device for identifying road traffic lights, electronic equipment and storage medium | |
| CN112477878B (en) | Driving decision sharing method, apparatus, device and medium for autonomous vehicle | |
| CN110738183B (en) | Road side camera obstacle detection method and device | |
| CN111767360B (en) | Method and device for marking virtual lane at intersection | |
| CN111693062B (en) | Method and device for navigation of roundabout route, electronic equipment and storage medium | |
| CN113635912B (en) | Vehicle control method, device, equipment, storage medium and automatic driving vehicle | |
| CN114572240B (en) | Vehicle running control method, device, vehicle, electronic device and storage medium | |
| CN112802326B (en) | Traffic scheme control method and device | |
| CN113723141A (en) | Vehicle positioning method and device, electronic equipment, vehicle and storage medium | |
| CN112859829B (en) | Vehicle control method and device, electronic equipment and medium | |
| CN111665845A (en) | Method, device, equipment and storage medium for planning path | |
| CN111361560B (en) | Method and device for controlling vehicle running in automatic driving and electronic equipment | |
| CN112164238A (en) | Navigation lane change guiding method, device, equipment and storage medium | |
| CN111553319A (en) | Method and device for acquiring information | |
| CN111353466A (en) | Lane line recognition processing method, lane line recognition processing device, and storage medium | |
| CN112802325A (en) | Vehicle queuing length detection method and device | |
| CN111666876A (en) | Method and device for detecting obstacle, electronic equipment and road side equipment | |
| CN111693059A (en) | Navigation method, device and equipment for roundabout and storage medium | |
| CN114413920A (en) | Lane data processing method, navigation method and device | |
| CN111637899A (en) | Navigation method, navigation device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |