CN114527758A - Path planning method and device, equipment, medium and product - Google Patents
Path planning method and device, equipment, medium and product Download PDFInfo
- Publication number
- CN114527758A CN114527758A CN202210174448.XA CN202210174448A CN114527758A CN 114527758 A CN114527758 A CN 114527758A CN 202210174448 A CN202210174448 A CN 202210174448A CN 114527758 A CN114527758 A CN 114527758A
- Authority
- CN
- China
- Prior art keywords
- path
- vehicle
- target
- obstacle
- path node
- 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.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 230000004044 response Effects 0.000 claims abstract description 27
- 230000008447 perception Effects 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims description 53
- 238000005457 optimization Methods 0.000 claims description 35
- 238000012216 screening Methods 0.000 claims description 32
- 238000004590 computer program Methods 0.000 claims description 11
- 230000009471 action Effects 0.000 claims description 10
- 230000003252 repetitive effect Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims 1
- 238000013473 artificial intelligence Methods 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 7
- 238000013480 data collection Methods 0.000 description 6
- 230000001133 acceleration Effects 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 230000004888 barrier function Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000004438 eyesight Effects 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000004297 night vision Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000452 restraining effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 230000016776 visual perception Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Optics & Photonics (AREA)
- Electromagnetism (AREA)
- Traffic Control Systems (AREA)
Abstract
The present disclosure provides a path planning method, apparatus, device, medium and product, which relate to the field of artificial intelligence, and in particular to the technical field of automatic driving and intelligent transportation. The specific implementation scheme comprises the following steps: determining obstacle description information associated with a vehicle driving space in response to the acquired environment perception information; determining a path planning space of a path to be generated in a vehicle driving space in response to the acquired vehicle information of the vehicle; determining a path constraint feature for constraining path generation based on the obstacle description information and the vehicle information of the vehicle; and generating a recommended driving path based on the path planning space according to the path constraint characteristics.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and in particular to the technical field of automatic driving and intelligent transportation, and can be applied to the scenes of path planning and the like.
Background
The path planning has important significance for ensuring safe driving of the vehicle and intelligent obstacle avoidance, and can provide decision support for control logic of vehicle auxiliary driving. However, in some scenarios, the path planning process has the phenomena of significant environmental limitation, low planning efficiency and poor planning effect.
Disclosure of Invention
The present disclosure provides a path planning method and apparatus, device, medium and product.
According to an aspect of the present disclosure, there is provided a path planning method, including: determining obstacle description information associated with a vehicle driving space in response to the acquired environment perception information; responding to the acquired vehicle information of the vehicle, and determining a path planning space of a path to be generated in the vehicle driving space; determining a path constraint feature for constraining path generation based on the obstacle description information and the vehicle information of the vehicle; and generating a recommended driving path based on the path planning space according to the path constraint characteristics.
According to another aspect of the present disclosure, there is provided a path planning apparatus including: the first processing module is used for responding to the acquired environment perception information and determining obstacle description information related to a vehicle running space; the second processing module is used for responding to the acquired vehicle information of the vehicle and determining a path planning space of a path to be generated in the vehicle driving space; a third processing module, configured to determine a path constraint feature for constraining path generation based on the obstacle description information and the vehicle information; and the fourth processing module is used for generating a recommended driving path based on the path planning space according to the path constraint characteristics.
According to another aspect of the present disclosure, there is provided 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 the path planning method described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the path planning method described above.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the path planning method described above.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 schematically illustrates a system architecture of a path planning method and apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow diagram of a path planning method according to an embodiment of the present disclosure;
fig. 3 schematically shows a flow chart of a path planning method according to another embodiment of the present disclosure;
FIG. 4 schematically shows a schematic diagram of a path planning process according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a path planner according to an embodiment of the present disclosure;
fig. 6 schematically shows a block diagram of an electronic device for performing path planning according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. 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 disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides a path planning method. The method comprises the following steps: the method comprises the steps of determining obstacle description information associated with a vehicle driving space in response to acquired environment perception information, determining a path planning space of a path to be generated in the vehicle driving space in response to acquired vehicle information of a vehicle, determining a path constraint feature for constraining path generation based on the obstacle description information and the vehicle information of the vehicle, and generating a recommended driving path based on the path planning space according to the path constraint feature.
Fig. 1 schematically shows a system architecture of a path planning method and apparatus according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
The system architecture 100 according to this embodiment may include a data collection side 101, a network 102, and a server 103. Network 102 is the medium used to provide a communication link between data collection end 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others. The server 103 may be an independent physical server, may be a server cluster or a distributed system formed by a plurality of physical servers, and may be a cloud server providing basic cloud computing services such as cloud services, cloud computing, network services, and middleware services.
The data acquisition terminal 101 interacts with the server 103 through the network 102 to receive or transmit data and the like. The data collection terminal 101 may be used to collect environmental perception information of a vehicle driving space, for example, where the environmental perception information may include visual perception information and ultrasonic perception information, for example. The data collection terminal 101 may also be used to collect host vehicle information, which may include, for example, coordinates of the center of mass of the vehicle, the yaw angle of the vehicle, the lateral/longitudinal speed of the vehicle, the lateral/longitudinal acceleration of the vehicle, and the like.
The server 103 may be a server providing various services, for example, a background processing server (for example only) performing path planning according to the environment perception information and the vehicle information provided by the data acquisition terminal 101.
For example, the server 103 determines obstacle description information associated with a vehicle travel space in response to environment perception information acquired from the data acquisition terminal 101, determines a path planning space of a path to be generated in the vehicle travel space in response to own vehicle information acquired from the data acquisition terminal 101, determines a path constraint feature for constraining path generation based on the obstacle description information and the own vehicle information, and generates a recommended travel path based on the path planning space according to the path constraint feature.
It should be noted that the path planning method provided by the embodiment of the present disclosure may be executed by the server 103. Accordingly, the path planning apparatus provided by the embodiment of the present disclosure may be disposed in the server 103. The path planning method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 103 and is capable of communicating with the data acquisition terminal 101 and/or the server 103. Correspondingly, the path planning apparatus provided in the embodiment of the present disclosure may also be disposed in a server or a server cluster that is different from the server 103 and can communicate with the data acquisition terminal 101 and/or the server 103.
It should be understood that the number of data collection terminals, networks, and servers in fig. 1 is merely illustrative. There may be any number of data collection terminals, networks, and servers, as desired for implementation.
The embodiment of the present disclosure provides a path planning method, and a path planning method according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 4 in conjunction with the system architecture of fig. 1. The path planning method of the embodiment of the present disclosure may be executed by the server 103 shown in fig. 1, for example.
Fig. 2 schematically shows a flow chart of a path planning method according to an embodiment of the present disclosure.
As shown in fig. 2, the path planning method 200 of the embodiment of the present disclosure may include, for example, operations S210 to S240.
In operation S210, obstacle description information associated with a vehicle driving space is determined in response to the acquired environment perception information.
In operation S220, a path planning space of a path to be generated in a vehicle driving space is determined in response to the acquired own vehicle information.
In operation S230, a path constraint feature for constraining the path generation is determined based on the obstacle description information and the own vehicle information.
In operation S240, a recommended travel path based on the path planning space is generated according to the path constraint feature.
An example flow of each operation of the path planning method of the present embodiment is illustrated below.
Illustratively, obstacle description information associated with the vehicle travel space is determined in response to the acquired environment perception information. The environment sensing information may be collected by a vehicle-mounted terminal or a roadside device, for example, and the vehicle-mounted terminal may include a laser sensing terminal, an ultrasonic sensing terminal, a millimeter wave sensing terminal, a camera system, an antenna positioning terminal, and the like.
The laser sensing terminal may for example comprise diagonally arranged lidar, which may for example be used for road edge identification, passable area extraction, obstacle clustering etc. The ultrasound-aware terminal may, for example, comprise an ultrasound radar arranged around the vehicle, which may be used, for example, for passable area extraction and obstacle clustering. The millimeter wave-aware terminals may include, for example, millimeter wave radars disposed around the vehicle, which may be used, for example, for passable region extraction and obstacle clustering.
The camera system can comprise various visual cameras, for example, binocular visual cameras, fisheye cameras, night vision cameras and the like, and can effectively realize full-coverage perception of vehicle vision without blind areas. The antenna positioning terminal can be used for receiving positioning information such as Beidou positioning, global positioning GPS, Gray and the like.
And responding to the acquired multi-source environment perception information, and performing fusion processing based on a preset standard data format, the same sampling frequency and the same coordinate system on the multi-source environment perception information to obtain fused environment perception information. And constructing a space communication area based on the fused environment perception information, and establishing an obstacle model in the vehicle driving space to obtain obstacle description information associated with the vehicle driving space.
Illustratively, from the environmental image captured by the binocular vision camera, an image edge detection algorithm is employed to identify lane lines and road edges in the image. And illustratively, according to the original laser point cloud obtained by the laser radar, removing the laser point cloud shielded by the vehicle body from the original laser point cloud through cutting operation to obtain the preprocessed laser point cloud. And according to the vehicle motion information, performing motion compensation on the preprocessed laser point cloud, and correcting the point cloud distortion caused by vehicle motion to obtain the compensated laser point cloud. And extracting the passable area, the road edge information, the obstacle information and other contents in the compensated laser point cloud by adopting a nearest neighbor clustering algorithm to obtain the obstacle description information associated with the vehicle driving space.
And determining a path planning space of a path to be generated in the vehicle driving space according to the acquired vehicle information of the vehicle. The vehicle information may be acquired by a global positioning GPS terminal, an inertial measurement unit IMU, a global navigation satellite system GNSS, or the like. The GPS terminal and GNSS can be used to acquire information such as the coordinates of the center of mass of the vehicle, the yaw angle of the vehicle, the lateral/longitudinal speed of the vehicle, and the like at the present time. The inertial measurement unit IMU may be used to acquire information such as a three-axis attitude angle and a three-axis acceleration of the vehicle at the present time, for example.
When the path planning space of the path to be generated in the vehicle driving space is determined, a path planning end point which meets a preset distance threshold value with the current position of the vehicle can be determined according to the current position of the vehicle indicated by the vehicle information of the vehicle, and the path planning space can be determined according to the current position of the vehicle and the path planning end point. Illustratively, a position point separated from the current position of the vehicle by a preset distance in the vehicle traveling direction is taken as the path planning end point.
The method has the advantages that the path planning space of the path to be generated is determined according to the current position of the vehicle and the path planning end point, the path planning efficiency is improved, the time complexity of path planning operation is reduced, in addition, the path planning precision is improved, and the limitation of the path length on the planning of the advancing path can be effectively broken through.
Based on the obstacle description information and the own-vehicle information, a path constraint feature for constraining the generation of the path is determined. According to the example mode, the obstacle avoidance constraint characteristics are determined according to the obstacle description information and the vehicle information of the vehicle. And estimating the vehicle situation according to the vehicle information to obtain a vehicle situation estimation result. And determining a path optimization feature for path screening according to the vehicle situation estimation result, wherein the obstacle avoidance constraint feature and the path optimization feature form a path constraint feature.
According to the obstacle description information and the vehicle information of the vehicle, the path constraint characteristics for constraining the path generation are determined, the accuracy of local path planning can be effectively improved, credible decision support can be provided for the control logic of vehicle auxiliary driving, and the safety driving of the unmanned vehicle can be guaranteed.
Illustratively, the obstacle avoidance decision can be made according to the information such as the obstacle position, the obstacle outline, the obstacle type, the obstacle danger level and the like indicated by the obstacle description information, so as to obtain the obstacle avoidance constraint characteristic. The obstacle avoidance constraint feature may indicate information such as an obstacle avoidance action type, an obstacle avoidance safety distance, an obstacle avoidance minimum distance, and the like, and the obstacle avoidance action type may include a lane following type, a left lane changing type, a right lane changing type, an overtaking type, and the like.
Further illustratively, the vehicle situation estimation result is obtained by performing vehicle situation estimation according to vehicle information of the vehicle, such as longitude and latitude coordinates of the vehicle, lateral/longitudinal speed of the vehicle, three-axis acceleration of the vehicle, and vehicle size, and the vehicle situation estimation result may indicate information of a predicted position of the vehicle, a heading angle of the vehicle, a predicted speed of the vehicle, a predicted acceleration of the vehicle, and the like, for example. And determining path optimization characteristics for path screening according to the vehicle situation estimation result, wherein the path optimization characteristics can be used for restraining path node curvature, path smoothness, path differential constraint, path node distance and other information.
The obstacle avoidance constraint feature and the path optimization feature constitute a path constraint feature for constraint path generation. And generating a recommended driving path based on the path planning space according to the determined path constraint characteristics. For example, a candidate path node set may be determined within the path planning space according to the obstacle avoidance constraint feature. And screening a plurality of target path nodes in the candidate path node set based on the obstacle avoidance constraint characteristic and the path optimization characteristic.
And smoothing the screened target path nodes to obtain a recommended driving path for vehicle auxiliary control. For example, a cubic bezier curve smoothing method is adopted to perform driving path fitting on a plurality of screened target path nodes, so as to obtain a recommended driving path meeting constraint conditions such as path node curve rate, obstacle safety distance, driving efficiency and control complexity.
And after the recommended driving path is determined, generating a driving control command according to the recommended driving path and the vehicle information of the vehicle. And sending a running control instruction to a vehicle control system so as to control the vehicle to automatically run based on the running control instruction. The travel control command includes, for example, command contents for control logics such as vehicle drive control, vehicle brake control, vehicle steering, and gear position.
With the disclosed embodiments, in response to the acquired environment awareness information, obstacle description information associated with a vehicle travel space is determined, in response to the acquired own vehicle information, a path planning space of a path to be generated in the vehicle travel space is determined, based on the obstacle description information and the own vehicle information, a path constraint feature for constraining path generation is determined, and a recommended travel path based on the path planning space is generated according to the path constraint feature.
According to the obstacle description information and the vehicle information of the vehicle, the path constraint characteristics for constraining the path generation are determined, the path planning aiming at various driving assistance scenes can be effectively realized, the limitation of factors such as the path length and the geographic position can be effectively broken through in the path planning process, and the universality of the path planning can be effectively improved. The system can effectively control the vehicle to safely avoid the barrier, and is favorable for ensuring the safe running of the unmanned vehicle.
Fig. 3 schematically shows a flow chart of a path planning method according to another embodiment of the present disclosure.
As shown in fig. 3, operation S240 may include, for example, operations S310 to S340.
In operation S310, a target subspace of the path to be generated is determined based on the path planning space according to the obstacle avoidance action type indicated by the obstacle avoidance constraint feature.
In operation S320, a candidate path node set is generated based on the target subspace according to the obstacle avoidance distance parameter indicated by the obstacle avoidance constraint feature.
In operation S330, a target path node is screened within the candidate path node set according to the path optimization feature.
In operation S340, it is determined whether the number of cycles reaches a preset threshold, or whether a distance between the screened latest target path node and the path planning end point is less than a preset threshold.
And returning to execute the operation S320 under the condition that the cycle times do not reach the preset threshold value and the distance between the screened latest target path node and the path planning end point is equal to or larger than the preset threshold value, otherwise, ending the operation.
An example flow of each operation of the path planning method of the present embodiment is illustrated below.
Illustratively, the type of obstacle avoidance action indicated by the obstacle avoidance constraint feature may include, for example, types of lane following, left lane changing, right lane changing, overtaking, and the like. And determining a target subspace of the path to be generated in the path planning space according to the obstacle avoidance action type.
The target subspace of the path to be generated is further determined in the path planning space, so that the time complexity and the operation amount of path planning operation are reduced, the path planning efficiency can be effectively improved, and the path planning effect is effectively improved.
And repeatedly and circularly executing the following operations until the repeated circulation times reach a preset threshold value, or the distance between the latest screened target path node and the path planning terminal is smaller than the preset threshold value, so as to obtain the recommended driving path formed by the target path node sequence. Generating a candidate path node set based on the target subspace according to the obstacle avoidance distance parameter indicated by the obstacle avoidance constraint characteristic; screening target path nodes in the candidate path node set according to the path optimization characteristics
According to the current position of the vehicle indicated by the vehicle information of the vehicle and the obstacle avoidance distance parameter matched with the current position of the vehicle, a candidate path node set associated with the current position of the vehicle is generated based on the target subspace. And screening the 1 st target path node in the candidate path node set associated with the current position of the vehicle according to the path optimization characteristics.
And responding to the screening of the 1 st target path node in the candidate path node set associated with the current position of the vehicle, and generating the candidate path node set associated with the 1 st target path node based on the target subspace according to the obstacle avoidance distance parameter matched with the 1 st target path node. And screening the 2 nd target path node in the candidate path node set associated with the 1 st target path node according to the path optimization characteristics.
And in response to screening out an N +1 th target path node from a candidate path node set associated with the nth target path node, generating a candidate path node set associated with the N +1 th target path node based on the target subspace according to the obstacle avoidance distance parameter matched with the N +1 th target path node, wherein N is 1. And screening the (n + 2) th target path node in the candidate path node set associated with the (n + 1) th target path node according to the path optimization characteristics.
The obstacle avoidance distance parameter indicates a minimum distance constraint value between the target path node and the obstacle point. Aiming at any target path node, the obstacle avoidance distance parameter matched with the target path node is obtained based on the following operations: determining a target obstacle with a distance to a target path node smaller than a preset safe distance threshold according to the obstacle description information; and taking the obstacle avoidance distance parameter corresponding to the obstacle mark as an obstacle avoidance distance parameter matched with the target path node according to the obstacle mark of the target obstacle.
The path optimization characteristics indicate constraint information of characteristics such as path node curve rate, driving efficiency, path length, vehicle turning radius, control complexity, path planning efficiency and the like for the recommended travel path. After the recommended driving path is determined, the recommended driving path and the vehicle information of the vehicle can be broadcasted to other traffic objects in a V2X (vehicle to x) communication mode, or to vehicle networking roadside devices and a vehicle networking platform.
By considering the dynamic property and uncertainty of the vehicle running environment, effective auxiliary driving decision support can be provided for the vehicle running environment with abundant structural characteristics, the limitation of factors such as path length, geographic position, terrain complexity and GPS signal strength on path planning can be effectively broken through, the adaptability of the path planning to dynamic complex scenes can be improved, and the universality of the path planning can be effectively improved.
Fig. 4 schematically shows a schematic diagram of a path planning process according to an embodiment of the present disclosure.
As shown in fig. 4, in response to the acquired environment awareness information, obstacle description information in the vehicle running space associated with the vehicle 4a is determined, the obstacle description information indicating that there is an obstacle 4b obstructing the travel in the vehicle traveling direction.
In response to acquiring the own-vehicle information associated with the vehicle 4a, the vehicle current position is determined. According to the current position of the vehicle, a path planning terminal 4c meeting a preset distance threshold with the current position of the vehicle is determined, and according to the current position of the vehicle and the path planning terminal 4c, a path planning space 4d is determined (as shown in fig. 4A).
And determining obstacle avoidance constraint characteristics according to the obstacle description information and the vehicle information of the vehicle. And determining that the type of the obstacle avoidance action to be performed by the vehicle 4a is a left-side detour type according to the obstacle avoidance constraint characteristics. Based on the left-hand detour type, a target subspace 4e of the path to be generated is determined within the path plan space 4d (as shown in fig. 4B).
The vehicle situation estimation for the vehicle 4a is performed according to the own vehicle information, and a vehicle situation estimation result is obtained. And determining path optimization characteristics for path screening according to the vehicle situation estimation result. The path optimization characteristics indicate constraint information for characteristics such as path node curve rate, driving efficiency, path length, vehicle turning radius, control complexity, and the like.
And generating a candidate path node set associated with the current position of the vehicle in the target subspace 4e according to the obstacle avoidance distance parameter indicated by the obstacle avoidance constraint feature and based on the current position of the vehicle indicated by the vehicle information and the obstacle avoidance distance parameter matched with the current position of the vehicle. And screening the 1 st target path node in the candidate path node set associated with the current position of the vehicle according to the path optimization characteristics.
And responding to the screening of the 1 st target path node in the candidate path node set associated with the current position of the vehicle, and generating the candidate path node set associated with the 1 st target path node in the target subspace 4e according to the obstacle avoidance distance parameter matched with the 1 st target path node. And screening the 2 nd target path node in the candidate path node set associated with the 1 st target path node according to the path optimization characteristics.
In response to the N +1 th target path node being screened out from the candidate path node set associated with the nth target path node, a candidate path node set associated with the N +1 th target path node is generated in the target subspace 4e according to the obstacle avoidance distance parameter matched with the N +1 th target path node, where N is 1. And screening the (n + 2) th target path node in the candidate path node set associated with the (n + 1) th target path node according to the path optimization characteristics.
Exemplarily, as shown in fig. 4C, for the target path node 4f, a candidate path node set (a plurality of path nodes within a dashed rectangle shown in fig. 4C) associated with the target path node 4f is generated within the target subspace 4e according to the obstacle avoidance distance parameter matched with the target path node 4 f.
According to the path optimization characteristics, target path nodes 4g are screened out from a candidate path node set associated with the target path nodes 4f, the minimum obstacle avoidance distance condition is met between the target path nodes 4g and an obstacle 4b, constraints of conditions such as path node curve rate, vehicle turning radius and control complexity are met between the target path nodes 4g and the current position of a vehicle, and constraints of conditions such as path node curve rate, driving efficiency, path length and path planning efficiency are met between the target path nodes 4f and the target path nodes 4 g.
After the N target path nodes are determined, path fitting is performed based on the N target path nodes to obtain a recommended travel path for the vehicle 4 a. And generating a driving control command according to the recommended driving path and the vehicle information. And sending a running control instruction to a vehicle control system so as to control the vehicle to automatically run based on the running control instruction.
According to the obstacle avoidance constraint characteristic and the path optimization characteristic, the recommended driving path is generated in the target subspace in the path planning space, the accuracy of local path planning is favorably improved, credible decision support can be provided for the control logic of vehicle auxiliary driving, the intelligent obstacle avoidance of the unmanned vehicle can be effectively assisted, and the safe driving of the unmanned vehicle is favorably ensured.
Fig. 5 schematically shows a block diagram of a path planning apparatus according to an embodiment of the present disclosure.
As shown in fig. 5, the path planning apparatus 500 of the embodiment of the present disclosure includes, for example, a first processing module 510, a second processing module 520, a third processing module 530, and a fourth processing module 540.
A first processing module 510, configured to determine obstacle description information associated with a vehicle driving space in response to the acquired environment perception information; the second processing module 520 is configured to determine a path planning space of a path to be generated in a vehicle driving space in response to the acquired vehicle information of the vehicle; a third processing module 530, configured to determine a path constraint feature for constraining path generation based on the obstacle description information and the vehicle information; and a fourth processing module 540, configured to generate a recommended driving path based on the path planning space according to the path constraint feature.
With the disclosed embodiments, in response to the acquired environment awareness information, obstacle description information associated with a vehicle travel space is determined, in response to the acquired own vehicle information, a path planning space of a path to be generated in the vehicle travel space is determined, based on the obstacle description information and the own vehicle information, a path constraint feature for constraining path generation is determined, and a recommended travel path based on the path planning space is generated according to the path constraint feature.
According to the obstacle description information and the vehicle information of the vehicle, the path constraint characteristics for constraining the path generation are determined, the path planning aiming at various driving assistance scenes can be effectively realized, the limitation of factors such as the path length and the geographic position can be effectively broken through in the path planning process, and the universality of the path planning can be effectively improved. The system can effectively control the vehicle to safely avoid the barrier, and is favorable for ensuring the safe running of the unmanned vehicle.
According to an embodiment of the present disclosure, the third processing module includes: the first processing submodule is used for determining obstacle avoidance constraint characteristics according to the obstacle description information and the vehicle information of the vehicle; the second processing submodule is used for carrying out vehicle situation estimation according to the vehicle information of the vehicle to obtain a vehicle situation estimation result; and the third processing submodule is used for determining the path optimization characteristics for path screening according to the vehicle situation estimation result. The obstacle avoidance constraint feature and the path optimization feature constitute a path constraint feature.
According to an embodiment of the present disclosure, the third processing sub-module includes: the first processing unit is used for determining a target subspace of the path to be generated based on the path planning space according to the obstacle avoidance action type indicated by the obstacle avoidance constraint characteristic; the second processing unit is used for repeatedly and circularly executing the following operations until the circulating times reach a preset threshold value, and obtaining a recommended travel path formed by the target path node sequence: generating a candidate path node set based on the target subspace according to an obstacle avoidance distance parameter indicated by an obstacle avoidance constraint characteristic; and screening target path nodes in the candidate path node set according to the path optimization characteristics.
According to an embodiment of the present disclosure, the second processing unit includes: the first processing subunit is used for generating a candidate path node set associated with the current position of the vehicle based on the target subspace according to the current position of the vehicle indicated by the vehicle information of the vehicle and an obstacle avoidance distance parameter matched with the current position of the vehicle; responding to the condition that the 1 st target path node is screened out from the candidate path node set associated with the current position of the vehicle, and generating a candidate path node set associated with the 1 st target path node based on the target subspace according to the obstacle avoidance distance parameter matched with the 1 st target path node; and in response to screening out an N +1 th target path node from the candidate path node set associated with the nth target path node, generating a candidate path node set associated with the N +1 th target path node based on the target subspace according to the obstacle avoidance distance parameter matched with the N +1 th target path node, wherein N is 1.
According to the embodiment of the disclosure, for any target path node, the obstacle avoidance distance parameter matched with the target path node is obtained based on the following operations: determining a target obstacle with a distance to a target path node smaller than a preset safe distance threshold according to the obstacle description information; and taking the obstacle avoidance distance parameter corresponding to the obstacle identification as the obstacle avoidance distance parameter matched with the target path node according to the obstacle identification of the target obstacle.
According to an embodiment of the present disclosure, the second processing unit includes: the second processing subunit is used for screening a 1 st target path node in a candidate path node set associated with the current position of the vehicle according to the path optimization characteristics; and screening an N +1 th target path node in a candidate path node set associated with the nth target path node according to the path optimization characteristics, wherein N is 1.
According to an embodiment of the present disclosure, the second processing module includes: the fourth processing submodule is used for determining a path planning terminal point which meets a preset distance threshold value with the current position of the vehicle according to the current position of the vehicle indicated by the vehicle information; and the fifth processing submodule is used for determining a path planning space according to the current position of the vehicle and the path planning terminal. The termination condition of the aforementioned repetitive cycle operation further includes: and the distance between the screened latest target path node and the path planning end point is smaller than a preset threshold value.
According to an embodiment of the present disclosure, the apparatus further includes a fifth processing module, configured to: generating a driving control instruction according to the recommended driving path and the vehicle information of the vehicle; and sending a running control instruction to a vehicle control system so as to control the vehicle to automatically run based on the running control instruction.
It should be noted that in the technical solutions of the present disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the related information are all in accordance with the regulations of the related laws and regulations, and do not violate the customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Fig. 6 schematically shows a block diagram of an electronic device for performing a path planning method according to an embodiment of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. The electronic device 600 is 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 disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as a path planning method. For example, in some embodiments, the path planning method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the path planning method described above may be performed. Alternatively, in other embodiments, the calculation unit 601 may be configured to perform the path planning method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), 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.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable path planner, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with an object, 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 an object; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which objects can provide input to the computer. Other kinds of devices may also be used to provide for interaction with an object; for example, feedback provided to the subject can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the object 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., an object computer having a graphical object interface or a web browser through which objects 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. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
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 disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. 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 disclosure should be included in the scope of protection of the present disclosure.
Claims (19)
1. A path planning method, comprising:
determining obstacle description information associated with a vehicle driving space in response to the acquired environment perception information;
responding to the acquired vehicle information of the vehicle, and determining a path planning space of a path to be generated in the vehicle driving space;
determining a path constraint feature for constraining path generation based on the obstacle description information and the own vehicle information; and
and generating a recommended driving path based on the path planning space according to the path constraint characteristics.
2. The method of claim 1, wherein said determining path constraint features for constraining path generation based on the obstacle description information and the host vehicle information comprises:
determining obstacle avoidance constraint characteristics according to the obstacle description information and the vehicle information of the vehicle;
estimating the vehicle situation according to the vehicle information to obtain a vehicle situation estimation result; and
determining path optimization characteristics for path screening according to the vehicle situation estimation result,
wherein the obstacle avoidance constraint feature and the path optimization feature constitute the path constraint feature.
3. The method of claim 2, wherein the generating a recommended travel path based on the path planning space according to the path constraint feature comprises:
determining a target subspace of a path to be generated based on the path planning space according to the obstacle avoidance action type indicated by the obstacle avoidance constraint feature;
and repeating the following operation in a circulating manner until the circulating times reach a preset threshold value, and obtaining a recommended travel path formed by the target path node sequence:
generating a candidate path node set based on the target subspace according to the obstacle avoidance distance parameter indicated by the obstacle avoidance constraint feature; and
and screening target path nodes in the candidate path node set according to the path optimization characteristics.
4. The method of claim 3, wherein the generating a set of candidate path nodes based on the target subspace according to an obstacle avoidance distance parameter indicated by the obstacle avoidance constraint feature comprises:
generating a candidate path node set associated with the current position of the vehicle based on the target subspace according to the current position of the vehicle indicated by the vehicle information and an obstacle avoidance distance parameter matched with the current position of the vehicle;
in response to screening a 1 st target path node from a candidate path node set associated with the current position of the vehicle, generating a candidate path node set associated with the 1 st target path node based on the target subspace according to an obstacle avoidance distance parameter matched with the 1 st target path node; and
in response to screening out an N +1 th target path node from a candidate path node set associated with an nth target path node, generating a candidate path node set associated with the N +1 th target path node based on the target subspace according to an obstacle avoidance distance parameter matched with the N +1 th target path node, wherein N is 1, and N is an integer greater than 1.
5. The method of claim 4, wherein for any target path node, the obstacle avoidance distance parameter matched with the target path node is obtained based on:
determining a target obstacle with a distance to the target path node smaller than a preset safe distance threshold according to the obstacle description information; and
and taking the obstacle avoidance distance parameter corresponding to the obstacle identification as an obstacle avoidance distance parameter matched with the target path node according to the obstacle identification of the target obstacle.
6. The method of claim 3, wherein the screening target path nodes within the set of candidate path nodes according to the path optimization feature comprises:
screening a 1 st target path node in a candidate path node set associated with the current position of the vehicle according to the path optimization characteristics; and
and screening an N +1 th target path node in a candidate path node set associated with an nth target path node according to the path optimization characteristics, wherein N is 1, and N is an integer greater than 1.
7. The method of claim 3, wherein,
the determining a path planning space of a path to be generated in the vehicle driving space in response to the acquired vehicle information of the vehicle includes:
determining a path planning terminal point meeting a preset distance threshold value with the current position of the vehicle according to the current position of the vehicle indicated by the vehicle information;
determining the path planning space according to the current position of the vehicle and the path planning terminal; and
the termination condition of the repetitive cycle operation further includes: and the distance between the screened latest target path node and the path planning end point is smaller than a preset threshold value.
8. The method of any of claims 1 to 7, further comprising:
generating a driving control instruction according to the recommended driving path and the vehicle information;
and sending the running control instruction to a vehicle control system so as to control the vehicle to automatically run based on the running control instruction.
9. A path planner, comprising:
the first processing module is used for responding to the acquired environment perception information and determining obstacle description information related to a vehicle running space;
the second processing module is used for responding to the acquired vehicle information of the vehicle and determining a path planning space of a path to be generated in the vehicle driving space;
a third processing module, configured to determine a path constraint feature for constraining path generation based on the obstacle description information and the vehicle information; and
and the fourth processing module is used for generating a recommended driving path based on the path planning space according to the path constraint characteristics.
10. The apparatus of claim 9, wherein the third processing module comprises:
the first processing submodule is used for determining obstacle avoidance constraint characteristics according to the obstacle description information and the vehicle information of the vehicle;
the second processing submodule is used for carrying out vehicle situation estimation according to the vehicle information of the vehicle to obtain a vehicle situation estimation result; and
a third processing submodule for determining a path optimization feature for path screening according to the vehicle situation estimation result,
wherein the obstacle avoidance constraint feature and the path optimization feature constitute the path constraint feature.
11. The apparatus of claim 10, wherein the third processing sub-module comprises:
the first processing unit is used for determining a target subspace of the path to be generated based on the path planning space according to the obstacle avoidance action type indicated by the obstacle avoidance constraint characteristic;
the second processing unit is used for repeatedly and circularly executing the following operations until the circulating times reach a preset threshold value, and obtaining a recommended travel path formed by the target path node sequence:
generating a candidate path node set based on the target subspace according to the obstacle avoidance distance parameter indicated by the obstacle avoidance constraint feature; and
and screening target path nodes in the candidate path node set according to the path optimization characteristics.
12. The apparatus of claim 11, wherein the second processing unit comprises:
the first processing subunit is configured to generate a candidate path node set associated with the current vehicle position based on the target subspace according to the current vehicle position indicated by the vehicle information and an obstacle avoidance distance parameter matched with the current vehicle position;
in response to screening a 1 st target path node from a candidate path node set associated with the current position of the vehicle, generating a candidate path node set associated with the 1 st target path node based on the target subspace according to an obstacle avoidance distance parameter matched with the 1 st target path node; and
in response to screening out an N +1 th target path node from a candidate path node set associated with an nth target path node, generating a candidate path node set associated with the N +1 th target path node based on the target subspace according to an obstacle avoidance distance parameter matched with the N +1 th target path node, wherein N is 1, and N is an integer greater than 1.
13. The apparatus of claim 12, wherein for any target path node, the obstacle avoidance distance parameter matching the target path node is obtained based on:
determining a target obstacle with a distance to the target path node smaller than a preset safe distance threshold according to the obstacle description information; and
and taking the obstacle avoidance distance parameter corresponding to the obstacle identification as an obstacle avoidance distance parameter matched with the target path node according to the obstacle identification of the target obstacle.
14. The apparatus of claim 11, wherein the second processing unit comprises:
the second processing subunit is used for screening a 1 st target path node in a candidate path node set associated with the current position of the vehicle according to the path optimization characteristics; and
and screening an N +1 th target path node in a candidate path node set associated with an nth target path node according to the path optimization characteristics, wherein N is 1, and N is an integer greater than 1.
15. The apparatus of claim 11, wherein,
the second processing module comprises:
the fourth processing submodule is used for determining a path planning terminal point which meets a preset distance threshold value with the current position of the vehicle according to the current position of the vehicle indicated by the vehicle information;
the fifth processing submodule is used for determining the path planning space according to the current position of the vehicle and the path planning terminal; and
the termination condition of the repetitive cycle operation further includes: and the distance between the screened latest target path node and the path planning end point is smaller than a preset threshold value.
16. The apparatus of any of claims 9 to 15, further comprising a fifth processing module to:
generating a driving control instruction according to the recommended driving path and the vehicle information;
and sending the running control instruction to a vehicle control system so as to control the vehicle to automatically run based on the running control instruction.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
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 1-8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-8.
19. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210174448.XA CN114527758A (en) | 2022-02-24 | 2022-02-24 | Path planning method and device, equipment, medium and product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210174448.XA CN114527758A (en) | 2022-02-24 | 2022-02-24 | Path planning method and device, equipment, medium and product |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114527758A true CN114527758A (en) | 2022-05-24 |
Family
ID=81624509
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210174448.XA Withdrawn CN114527758A (en) | 2022-02-24 | 2022-02-24 | Path planning method and device, equipment, medium and product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114527758A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107702716A (en) * | 2017-08-31 | 2018-02-16 | 广州小鹏汽车科技有限公司 | A kind of unmanned paths planning method, system and device |
| CN111186443A (en) * | 2020-02-06 | 2020-05-22 | 北京百度网讯科技有限公司 | Lane change path planning method and device, electronic equipment and computer readable medium |
| US20200272157A1 (en) * | 2019-02-22 | 2020-08-27 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method and apparatus for planning travelling path, and vehicle |
| CN111982143A (en) * | 2020-08-11 | 2020-11-24 | 北京汽车研究总院有限公司 | Vehicle and vehicle path planning method and device |
| CN112578788A (en) * | 2019-09-30 | 2021-03-30 | 北京百度网讯科技有限公司 | Vehicle obstacle avoidance quadratic programming method, device, equipment and readable storage medium |
| CN112572424A (en) * | 2019-09-11 | 2021-03-30 | 北京百度网讯科技有限公司 | Vehicle control method, device, equipment and medium based on obstacle recognition |
| US20210116916A1 (en) * | 2019-10-22 | 2021-04-22 | Baidu Usa Llc | End dynamics and constraints relaxation algorithm on optimizing an open space trajectory |
| WO2021142793A1 (en) * | 2020-01-17 | 2021-07-22 | 华为技术有限公司 | Path planning method and path planning apparatus |
| CN113932826A (en) * | 2021-10-13 | 2022-01-14 | 北京经纬恒润科技股份有限公司 | Optimization method and system for vehicle navigation path |
-
2022
- 2022-02-24 CN CN202210174448.XA patent/CN114527758A/en not_active Withdrawn
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107702716A (en) * | 2017-08-31 | 2018-02-16 | 广州小鹏汽车科技有限公司 | A kind of unmanned paths planning method, system and device |
| US20200149906A1 (en) * | 2017-08-31 | 2020-05-14 | Guangzhou Xiaopeng Motors Technology Co., Ltd. | Path planning method, system and device for autonomous driving |
| US20200272157A1 (en) * | 2019-02-22 | 2020-08-27 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method and apparatus for planning travelling path, and vehicle |
| CN112572424A (en) * | 2019-09-11 | 2021-03-30 | 北京百度网讯科技有限公司 | Vehicle control method, device, equipment and medium based on obstacle recognition |
| CN112578788A (en) * | 2019-09-30 | 2021-03-30 | 北京百度网讯科技有限公司 | Vehicle obstacle avoidance quadratic programming method, device, equipment and readable storage medium |
| US20210116916A1 (en) * | 2019-10-22 | 2021-04-22 | Baidu Usa Llc | End dynamics and constraints relaxation algorithm on optimizing an open space trajectory |
| WO2021142793A1 (en) * | 2020-01-17 | 2021-07-22 | 华为技术有限公司 | Path planning method and path planning apparatus |
| CN111186443A (en) * | 2020-02-06 | 2020-05-22 | 北京百度网讯科技有限公司 | Lane change path planning method and device, electronic equipment and computer readable medium |
| CN111982143A (en) * | 2020-08-11 | 2020-11-24 | 北京汽车研究总院有限公司 | Vehicle and vehicle path planning method and device |
| CN113932826A (en) * | 2021-10-13 | 2022-01-14 | 北京经纬恒润科技股份有限公司 | Optimization method and system for vehicle navigation path |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11217012B2 (en) | System and method for identifying travel way features for autonomous vehicle motion control | |
| US10229363B2 (en) | Probabilistic inference using weighted-integrals-and-sums-by-hashing for object tracking | |
| US11175668B2 (en) | Navigation method and apparatus, and terminal device | |
| CN113264066B (en) | Obstacle track prediction method and device, automatic driving vehicle and road side equipment | |
| EP3553752A1 (en) | Information processing apparatus, information processing method, and computer-readable medium for generating an obstacle map | |
| CN115540896B (en) | Path planning method and device, electronic equipment and computer readable medium | |
| CN112560680A (en) | Lane line processing method and device, electronic device and storage medium | |
| CN113378693B (en) | Method and device for generating target detection system and detecting target | |
| CN114758502B (en) | Dual-vehicle combined track prediction method and device, electronic equipment and automatic driving vehicle | |
| CN115879060B (en) | Multi-mode-based automatic driving perception method, device, equipment and medium | |
| CN115761702B (en) | Vehicle track generation method, device, electronic equipment and computer readable medium | |
| WO2024012211A1 (en) | Autonomous-driving environmental perception method, medium and vehicle | |
| CN114047760B (en) | Path planning method and device, electronic equipment and automatic driving vehicle | |
| CN114724116B (en) | Vehicle traffic information generation method, device, equipment and computer readable medium | |
| CN114394111B (en) | Lane changing method for automatic driving vehicle | |
| CN116215517A (en) | Collision detection method, device, apparatus, storage medium, and autonomous vehicle | |
| CN114527758A (en) | Path planning method and device, equipment, medium and product | |
| US11544899B2 (en) | System and method for generating terrain maps | |
| CN114581869A (en) | Method and device for determining position of target object, electronic equipment and storage medium | |
| CN114689069A (en) | Navigation route processing method and device of automatic driving equipment and electronic equipment | |
| CN114030483A (en) | Vehicle control method, device, electronic apparatus, and medium | |
| CN115294764B (en) | Crosswalk area determination method, crosswalk area determination device, crosswalk area determination equipment and automatic driving vehicle | |
| CN114407916B (en) | Vehicle control and model training method and device, vehicle, equipment and storage medium | |
| CN114584949B (en) | Method and equipment for determining attribute value of obstacle through vehicle-road cooperation and automatic driving vehicle | |
| CN115649184A (en) | Vehicle control instruction generation method, device and equipment |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220524 |
|
| WW01 | Invention patent application withdrawn after publication |