CN115060279A - Path planning method and device, electronic equipment and computer readable medium - Google Patents
Path planning method and device, electronic equipment and computer readable medium Download PDFInfo
- Publication number
- CN115060279A CN115060279A CN202210640790.4A CN202210640790A CN115060279A CN 115060279 A CN115060279 A CN 115060279A CN 202210640790 A CN202210640790 A CN 202210640790A CN 115060279 A CN115060279 A CN 115060279A
- Authority
- CN
- China
- Prior art keywords
- destination
- determining
- vehicle
- grid
- obstacle
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000004364 calculation method Methods 0.000 claims abstract description 5
- 230000008569 process Effects 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 description 9
- 238000010276 construction Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000007613 environmental effect Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000001960 triggered effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000029305 taxis 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
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
-
- 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
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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 invention discloses a path planning method, a path planning device, electronic equipment and a computer readable medium, and relates to the technical field of automatic driving. The method comprises the following steps: determining information of an obstacle between a current location of the vehicle and a destination of the vehicle; generating a grid area around a destination, and determining a first cost of each grid according to a preset first rule and information of obstacles; determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid; and sequentially judging whether the track curves between the alternative destinations and the current position of the vehicle meet preset conditions or not according to the sequence of the second prices from small to large until the track curves meeting the preset conditions are determined, and taking the track curves meeting the preset conditions as the running path of the vehicle. The method can guarantee driving safety while guaranteeing accurate stop at the station, has small calculation amount and guarantees the real-time performance of path planning.
Description
Technical Field
The present invention relates to the field of automatic driving technologies, and in particular, to a path planning method and apparatus, an electronic device, and a computer-readable medium.
Background
In an automatic driving scene, an unmanned vehicle stop station is a common task scene, and is particularly common in the fields of unmanned taxis and unmanned buses. In an automatic driving scenario, a driving path needs to be planned for the unmanned vehicle according to environmental information. In the related art, the path plan can be divided into a global path plan and a local path plan according to the degree of grasp of the environmental information. The global path planning needs to master all environment information and carries out path planning according to all the information of the environment map; the local path planning only needs to acquire environmental information in real time by a sensor, know the environmental map information, and then determine the position of the map and the local obstacle distribution condition, so that the optimal path from the current node to a certain sub-destination node can be selected.
Disclosure of Invention
In view of this, embodiments of the present invention provide a path planning method, an apparatus, an electronic device, and a computer readable medium, which can guarantee driving safety, effectively reduce the amount of computation, and guarantee the real-time performance of path planning while guaranteeing smooth completion of a station-dependent task.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a path planning method including:
determining information of an obstacle between a current location of a vehicle and a destination of the vehicle;
generating a grid area around the destination, and determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle; wherein the first price is used to indicate a feasibility of the grid;
determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid;
and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence of the second cost from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
Optionally, generating a grid area around the destination comprises: determining a trajectory profile between a current location of the vehicle and a destination of the vehicle; detecting whether the track curve collides with the obstacle according to the information of the obstacle; if yes, generating a grid area around the destination; if not, taking a track curve between the current position of the vehicle and the destination of the vehicle as the driving path of the vehicle.
Optionally, the information of the obstacle includes position information of the obstacle;
determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle, including: for each grid, determining whether the grid collides with the obstacle according to the information of the obstacle; if so, determining that the first cost of the grid is a preset value; if not, determining a first distance between the grid and the obstacle according to the position information of the obstacle; determining a second distance between the grid and the destination; determining a first cost of the mesh according to the first distance and the second distance.
Optionally, determining a first cost of the mesh according to the first distance and the second distance includes: determining a smallest first distance from a plurality of first distances between the grid and the plurality of obstacles if the number of obstacles is plural; determining a first cost of the mesh based on the minimum first distance and the second distance.
Optionally, determining a plurality of alternative destinations around the destination comprises: generating a target graph with the destination as the center, wherein the range covered by the target graph and the range covered by the grid area are overlapped; and sampling on the target graph, and taking a sampling point as the alternative destination.
Optionally, determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid, including: for each alternative destination, determining a first cost of a grid where the alternative destination is located; determining a third distance between the alternate destination and the current location of the vehicle; and determining a second cost of the alternative destination according to the first cost and the third distance.
Optionally, the method further comprises: determining invalid alternatives in the plurality of alternative destinations and screening out the invalid alternatives before judging whether a track curve between the alternative destinations and the current position of the vehicle meets a preset condition.
Optionally, determining invalid alternatives in the plurality of alternative destinations comprises: for each alternative destination, determining whether the grid where the alternative destination is located collides with the obstacle; if so, determining that the alternative destination is an invalid alternative.
Optionally, the method further comprises: if the track curves between all the alternative destinations and the current position of the vehicle do not meet the preset condition, determining a new alternative destination, and judging whether the track curve between the new alternative destination and the current position of the vehicle meets the preset condition or not according to the sequence of the second generation price of the new alternative destination from small to large.
Optionally, the method further comprises: determining whether an obstacle moves in the process that the automatic driving vehicle runs according to the running path; if so, replanning the driving path of the vehicle according to the information of the moved obstacle.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a path planning apparatus including:
an obstacle determination module to determine information of an obstacle between a current location of a vehicle and a destination of the vehicle;
the first calculation module is used for generating a grid area around the destination and determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle; wherein the first price is used to indicate a feasibility of the grid;
a second calculation module, configured to determine a plurality of candidate destinations around the destination, and determine a second cost for each candidate destination according to a preset second rule and the first cost of the grid;
and the path determining module is used for sequentially judging whether a track curve between the alternative destination and the current position of the vehicle meets a preset condition according to the sequence of the second cost from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
Optionally, the first computing module is further configured to: determining a trajectory profile between a current location of the vehicle and a destination of the vehicle; detecting whether the track curve collides with the obstacle according to the information of the obstacle; if yes, generating a grid area around the destination; if not, taking a track curve between the current position of the vehicle and the destination of the vehicle as the driving path of the vehicle.
Optionally, the information of the obstacle includes position information of the obstacle;
the first computing module is further to: for each grid, determining whether the grid collides with the obstacle according to the information of the obstacle; if so, determining that the first cost of the grid is a preset value; if not, determining a first distance between the grid and the obstacle according to the position information of the obstacle; determining a second distance between the grid and the destination; determining a first cost of the mesh according to the first distance and the second distance.
Optionally, the first computing module is further configured to: determining a smallest first distance from a plurality of first distances between the grid and the plurality of obstacles if the number of obstacles is plural; determining a first cost of the mesh based on the minimum first distance and the second distance.
Optionally, the second computing module is further configured to: generating a target graph with the destination as the center, wherein the range covered by the target graph is overlapped with the range covered by the grid area; and sampling on the target graph, and taking a sampling point as the alternative destination.
Optionally, the second computing module is further configured to: for each alternative destination, determining a first cost of a grid where the alternative destination is located; determining a third distance between the alternate destination and the current location of the vehicle; and determining a second cost of the alternative destination according to the first cost and the third distance.
Optionally, the apparatus further comprises a screening module for: determining an invalid alternative of the plurality of alternative destinations and screening the invalid alternative.
Optionally, the screening module is further configured to: for each alternative destination, determining whether the grid where the alternative destination is located collides with the obstacle; if so, determining that the alternative destination is an invalid alternative.
Optionally, the apparatus further comprises a retry module configured to: if the track curves between all the alternative destinations and the current position of the vehicle do not meet the preset condition, determining a new alternative destination, and judging whether the track curve between the new alternative destination and the current position of the vehicle meets the preset condition or not according to the sequence of the second generation price of the new alternative destination from small to large.
Optionally, the retry module is further configured to: determining whether the obstacle moves or not in the process that the automatic driving vehicle runs according to the running path; if so, replanning the driving path of the vehicle according to the information of the moved obstacle.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus including: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the path planning method of the embodiment of the invention.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a computer-readable medium on which a computer program is stored, the program implementing the path planning method of the embodiments of the present invention when executed by a processor.
One embodiment of the above invention has the following advantages or benefits:
generating a grid area around a vehicle destination, and determining a first cost of each grid in the grid area according to information of obstacles between the current position of the vehicle and the destination and a preset first rule; the first price is used for indicating the feasibility degree of the grid, and the smaller the first price is, the higher the feasibility degree is, the safer the driving is, so that a safe area and a dangerous area in a grid area can be determined; then determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid; and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence from small to large of the second price until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the driving path of the vehicle, so that the driving safety can be ensured while the vehicle is accurately stopped at a station, the calculated amount is small, and the real-time performance of path planning is ensured.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 schematically shows a flow chart of a path planning method of an embodiment of the invention;
fig. 2 is a schematic diagram illustrating a mesh area in the path planning method according to the embodiment of the present invention;
FIG. 3 shows a schematic diagram of an alternative destination in the path planning method of an embodiment of the invention;
FIG. 4 schematically illustrates a flow chart of a path planning method according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of a path planning apparatus according to an embodiment of the present invention;
fig. 6 schematically shows a structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention 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 invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 schematically shows a flowchart of a path planning method according to an embodiment of the present invention, where the path planning method according to the embodiment of the present invention may be applied to a vehicle-mounted control unit of a vehicle, and may also be applied to a server, and the server may issue a driving path of the vehicle to the vehicle after determining the driving path. As shown in fig. 1, the method includes:
step 101: information of an obstacle between a current location of a vehicle and a destination of the vehicle is determined.
In this embodiment, the destination of the vehicle may be determined according to a specific scenario, for example, the task end point (i.e., the destination) may be obtained on a map by the parking task, and is issued to the vehicle through the cloud. The current position of the vehicle can be acquired from the positioning information of the on-board integrated navigation module which is preset on the vehicle. The information of the obstacle may be acquired by an in-vehicle sensor, such as a mono/binocular camera, a fisheye camera, a millimeter wave radar, an ultrasonic radar, a laser radar, or the like. The obstacle may include, for example, a pedestrian, an object on a road surface, a construction section, a bumpy road surface, and the like, and the present invention is not limited thereto. The information of the obstacle may include, but is not limited to: the location of the obstacle, the state of the obstacle (e.g., stationary or moving), the type of obstacle (e.g., pedestrian, object, construction road segment, etc.). The information of the obstacle can also be obtained from a map, for example, if a construction section exists on a section where the destination is located, the information of the construction section can be obtained through marking information on the map. The vehicle in the embodiment of the present invention may be an automatic driving vehicle (also referred to as an unmanned vehicle), such as an unmanned taxi, an unmanned bus, etc., and the present invention is not limited herein.
Step 102: generating a grid area around the destination, and determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacles; wherein the first price is used to indicate a feasibility of the grid.
The grid area generated around the destination may be a positive direction, a rectangle, a circle, or the like, and the present invention is not limited thereto. As an example, as shown in fig. 2, the grid area is square, and the grid within the grid area is also square. In the embodiment of the present invention, the size of the grid area and the size of the grid may be flexibly set according to the vehicle parameters, the scene task, and the environment of the destination, which is not limited herein. As an example, in a parking pick-up scenario, the side length of the grid may be set to 0.5 meters to allow for both vehicle speed and driving safety. After the grid area is generated, obstacles between the current location of the vehicle and the destination may be marked within the grid area.
After the grid region is generated, a first cost of each grid in the grid region is calculated, the first cost is used for indicating the feasibility of the grid, and the lower the cost is, the higher the feasibility is, and the more suitable the vehicle is for driving. If the grid collides with an obstacle, the grid danger is determined, and the first price of the grid is set to a preset value, for example, infinity. When judging whether the grid collides with the obstacle, the obstacle can be converted into a rectangle (the obstacle completely falls in the rectangle), then whether the grid and the rectangle have an overlapping part is determined according to the hyperplane segmentation theorem, and if the grid and the rectangle have the overlapping part, the grid and the obstacle are determined to collide.
For a mesh that has not collided with an obstacle, its first cost may be determined according to the following procedure:
determining a first distance between the grid and the obstacle according to the position information of the obstacle;
determining a second distance between the grid and the destination;
determining a first cost of the mesh according to the first distance and the second distance.
Wherein the first distance between the grid and the obstacle may be a distance between a center of the grid and a center of the obstacle. The smaller the first distance, the more dangerous the vehicle will travel on this grid. The second distance between the grid and the destination may be a distance between a center of the grid and the destination, with the smaller the second distance, the more efficient. After determining the first distance and the second distance, the first cost of the mesh may be calculated according to a preset cost function.
As an example, the preset cost function may be as shown in the following equation (1):
cost_net＝obstacle_distance-target_distance(1)
where cost _ net represents a first cost of the mesh, obstacle _ distance represents a first distance, and target _ distance represents a second distance.
In an alternative embodiment of the present invention, a weighting factor corresponding to the first distance and/or the second distance may be set when calculating the first cost of the mesh, as shown in the following equation (2):
cost_net＝k_obstacle*obstacle_distance-k_target*target_distance(2)
where k _ obstacle represents a weight coefficient corresponding to the first distance, and k _ target represents a weight coefficient corresponding to the second distance. Since the unit of the obstacle _ distance is the same as that of the target _ distance, the unit of k _ obstacle _ distance may be set to 1 by default, which means that the weight of the obstacle is the same as that of the target, and the adjustment may be performed according to the details of the task.
Step 103: determining a plurality of alternative destinations around the destination, and determining a second cost for each of the alternative destinations according to a preset second rule and the first cost of the mesh.
Wherein the alternative destination may be determined according to the following procedure:
generating a target graph with the destination as the center, wherein the range covered by the target graph is overlapped with the range covered by the grid area;
and sampling on the target graph, and taking a sampling point as the alternative destination.
The target graphic centered on the destination may completely fall within the grid area or partially fall within the grid area. The target pattern may be circular, square, rectangular, etc., and the present invention is not limited thereto. In an alternative embodiment, as shown in fig. 3, a circle may be generated with the destination as the center and the side length of the mesh as the radius (assuming the side length of the mesh is r). After generating a circle centered on the destination, the circle may be uniformly sampled, with the sample point as the alternate destination.
Step 104: and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence of the second cost from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
The preset condition may include, but is not limited to, a minimum turning radius of the vehicle.
In an alternative embodiment, the alternative destinations may be ranked in order of the second cost from small to large, and then a loop of the following process may be performed in the ranking until the vehicle's travel path is determined or all of the alternative destinations are traversed:
generating a track curve between the current position of the vehicle and the alternative destination by utilizing a cubic polynomial, a quintic polynomial or a heptatic polynomial;
judging whether the track curve meets a preset condition or not;
if the track curve meets the preset condition, taking the track curve as the driving path of the vehicle;
if not, judging the next alternative destination.
In an optional embodiment, a trajectory curve of the current position of the vehicle and the alternative destination may also be generated according to the Reed-Shepps curve, and if the trajectory curve can be successfully generated, the trajectory curve is determined to be the driving path of the vehicle.
According to the path planning method provided by the embodiment of the invention, a grid area is generated around a vehicle destination, and a first cost of each grid in the grid area is determined according to the information of obstacles between the current position of the vehicle and the destination and a preset first rule; the first price is used for indicating the feasibility degree of the grid, and the smaller the first price is, the higher the feasibility degree is, the safer the driving is, so that a safe area and a dangerous area in a grid area can be determined; then determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid; and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence from small to large of the second cost until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle, so that the running safety can be ensured while the vehicle is accurately stopped at a station, the calculated amount is small, and the real-time property of path planning is ensured.
The path planning method provided by the embodiment of the invention can determine the triggering mode according to different triggering conditions or triggering modes, for example, according to specific sensor configuration and task scenes, and can plan the path for the vehicle according to the method when the triggering conditions are met. As an example, path planning may be triggered in two ways: one is that after the environmental state near the destination is updated, a path planning task is triggered, which is commonly found in a reception task with a complex environment; the other is a path planning task triggered periodically, for example, a local path planning task is run every 100 milliseconds, and is often used in a scenario where the update frequency of multiple sensors is inconsistent.
Fig. 4 schematically shows a flowchart of a path planning method according to another embodiment of the present invention, and as shown in fig. 4, the method includes:
step 401: determining information of an obstacle between a current location of a vehicle and a destination of the vehicle;
step 402: determining a trajectory profile between a current location of the vehicle and a destination of the vehicle;
step 403: detecting whether the track curve collides with the obstacle according to the information of the obstacle;
step 404: if not, taking the track curve as a running path of the vehicle;
step 405: if yes, generating a grid area around the destination, and determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacles; wherein the first price is used to indicate a feasibility of the grid;
step 406: determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid;
step 407: and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence of the second cost from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
Wherein, the steps 401, 405 and 407 are the same as those in the embodiment shown in fig. 1, and the invention is not repeated herein to avoid repetition.
For step 402-. Then, the attitude of the vehicle on the track curve can be predicted, and then whether the vehicle collides with the obstacle when running on the track curve is determined according to the attitude of the vehicle on the track curve and the information of the obstacle, and if so, the track curve is determined to collide with the obstacle. When determining whether the vehicle will collide with the obstacle when running on the trajectory curve, the vehicle may be determined to collide with the obstacle when running on the trajectory curve according to whether the coverage of the vehicle in the pose overlaps with the coverage of the obstacle, and if so, the vehicle may be determined to run on the trajectory curve and collide with the obstacle. If the track curve does not collide with the obstacle, the track curve can be used as a driving path of the vehicle, so that the following steps are omitted, the calculated amount is effectively reduced, and the driving safety is guaranteed while the parking task is finished.
In an optional embodiment, before determining whether a trajectory curve between the candidate destination and the current position of the vehicle satisfies a preset condition, the path planning method further includes: determining invalid alternatives in the plurality of alternative destinations and screening out the invalid alternatives. Wherein the process of determining invalid alternatives in the plurality of alternative destinations may comprise:
for each alternative destination, determining whether the grid where the alternative destination is located collides with the obstacle; determining whether the first price of the grid where the alternative destination is located is a preset value;
if so, determining that the alternative destination is an invalid alternative.
The embodiment can avoid the collision of the planned driving path and the barrier by screening out invalid alternatives in the alternative destinations, thereby ensuring the driving safety of the vehicle.
In an optional embodiment, the path planning method further includes: and if the track curves between all the alternative destinations and the current position of the vehicle do not meet the preset condition, determining a new alternative destination, and judging whether the track curve between the new alternative destination and the current position of the vehicle meets the preset condition. For example, a new target graphic may be generated centered on the destination, and sampled on the new target graphic to obtain a new alternate destination. Taking fig. 3 as an example, a new circle may be generated with the destination as the center and 2r as the radius, and the new circle is sampled to obtain a new alternative destination. After the new alternative destinations are determined, calculating a second price of each new alternative destination, sequentially judging whether a track curve between the new alternative destinations and the current position of the vehicle meets a preset condition according to the sequence of the second prices from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
In an optional embodiment, the path planning method further includes: determining whether an obstacle moves in the process that the automatic driving vehicle runs according to the running path; if yes, replanning the driving path of the vehicle according to the information of the moved obstacle, namely, re-executing step 101-104 or step 401-407. Wherein the condition of the obstacle moving comprises one or more of: the obstacle moves from the first mesh to the second mesh, the obstacle moves from inside the mesh area to outside the mesh area, and the new obstacle moves from outside the mesh area to inside the mesh area. The path planning method of the embodiment detects whether the obstacle moves in real time in the driving process of the automobile, and plans the driving path of the automobile again if the obstacle moves, so that the automobile can be safely driven to the destination.
Fig. 5 schematically shows a schematic structural diagram of a path planning apparatus 500 according to an embodiment of the present invention, and as shown in fig. 5, the path planning apparatus 500 includes:
an obstacle determination module 501 for determining information of an obstacle between a current position of a vehicle and a destination of the vehicle;
a first calculating module 502, configured to generate a grid area around the destination, and determine a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle; wherein the first price is used to indicate a feasibility of the grid;
a second calculating module 503, configured to determine a plurality of alternative destinations around the destination, and determine a second cost of each alternative destination according to a preset second rule and the first cost of the grid;
a path determining module 504, configured to determine, according to a descending order of the second cost, whether a trajectory curve between the candidate destination and the current position of the vehicle meets a preset condition, and if yes, take the trajectory curve as a driving path of the vehicle.
The path planning device of the embodiment of the invention generates a grid area around a vehicle destination, and determines a first cost of each grid in the grid area according to the information of obstacles between the current position of the vehicle and the destination and a preset first rule; the first price is used for indicating the feasibility degree of the grid, and the smaller the first price is, the higher the feasibility degree is, the safer the driving is, so that a safe area and a dangerous area in a grid area can be determined; then determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid; and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence from small to large of the second cost until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle, so that the running safety can be ensured while the vehicle is accurately stopped at a station, the calculated amount is small, and the real-time property of path planning is ensured.
Optionally, the first computing module is further configured to: determining a trajectory profile between a current location of the vehicle and a destination of the vehicle; detecting whether the track curve collides with the obstacle according to the information of the obstacle; if so, generating a grid area around the destination.
Optionally, the information of the obstacle includes position information of the obstacle;
the first computing module is further to: for each mesh, determining a second distance between the mesh and the destination; determining a first distance between the grid and the obstacle according to the position information of the obstacle; determining a first cost of the mesh according to the first distance and the second distance.
Optionally, the first computing module is further configured to: determining a minimum first distance from a plurality of first distances between the grid and the plurality of obstacles when the number of obstacles is plural; determining a first cost of the mesh based on the minimum first distance and the second distance.
Optionally, the second computing module is further configured to: generating a target graph with the destination as the center, wherein the range covered by the target graph is overlapped with the range covered by the grid area; and sampling on the target graph, and taking a sampling point as the alternative destination.
Optionally, the second computing module is further configured to: for each alternative destination, determining a first cost of a grid where the alternative destination is located; determining a third distance between the alternate destination and the current location of the vehicle; and determining a second cost of the alternative destination according to the first cost and the third distance.
Optionally, the apparatus further comprises a screening module for: determining invalid alternatives in the plurality of alternative destinations and screening out the invalid alternatives.
Optionally, the screening module is further configured to: for each alternative destination, determining whether the grid where the alternative destination is located collides with the obstacle; if so, determining that the alternative destination is an invalid alternative.
Optionally, the apparatus further comprises a retry module configured to: if the track curves between all the alternative destinations and the current position of the vehicle do not meet the preset condition, determining a new alternative destination, and judging whether the track curve between the new alternative destination and the current position of the vehicle meets the preset condition or not according to the sequence of the second generation price of the new alternative destination from small to large.
Optionally, the retry module is further configured to: determining whether the obstacle moves or not in the process that the automatic driving vehicle runs according to the running path; if so, replanning the driving path of the vehicle according to the information of the moved obstacle.
The device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
An embodiment of the present invention further provides an electronic device, as shown in fig. 6, including one or more processors 601, a communication interface 602, a storage device 603, and a communication bus 604, where the processors 601, the communication interface 602, and the storage device 603 are in communication with each other through the communication bus 604,
a storage 603 for storing one or more programs;
the processor 601, when executing the program stored in the storage device 603, implements the following steps:
determining information of an obstacle between a current location of a vehicle and a destination of the vehicle;
generating a grid area around the destination, and determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle; wherein the first price is used to indicate a feasibility of the grid;
determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid;
and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence of the second cost from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The storage device may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one magnetic disk storage device. Optionally, the storage device may also be at least one storage device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In yet another embodiment of the present invention, a computer-readable storage medium is further provided, which has instructions stored therein, and when the instructions are executed on a computer, the instructions cause the computer to execute the path planning method according to any one of the above embodiments.
In a further embodiment provided by the present invention, there is also provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform the path planning method of any of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (13)
1. A method of path planning, comprising:
determining information of an obstacle between a current location of a vehicle and a destination of the vehicle;
generating a grid area around the destination, and determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle; wherein the first price is used to indicate a feasibility of the grid;
determining a plurality of alternative destinations around the destination, and determining a second cost of each alternative destination according to a preset second rule and the first cost of the grid;
and sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition or not according to the sequence of the second cost from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
2. The method of claim 1, wherein generating a grid area around the destination comprises:
determining a trajectory profile between a current location of the vehicle and a destination of the vehicle;
detecting whether the track curve collides with the obstacle according to the information of the obstacle;
if yes, generating a grid area around the destination;
if not, taking a track curve between the current position of the vehicle and the destination of the vehicle as the driving path of the vehicle.
3. The method of claim 1, wherein the information of the obstacle comprises position information of the obstacle;
determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle, including:
for each grid, determining whether the grid collides with the obstacle according to the information of the obstacle;
if so, determining that the first cost of the grid is a preset value;
if not, determining a first distance between the grid and the obstacle according to the position information of the obstacle; determining a second distance between the grid and the destination; determining a first cost of the mesh according to the first distance and the second distance.
4. The method of claim 3, wherein determining the first cost for the mesh based on the first distance and the second distance comprises:
determining a smallest first distance from a plurality of first distances between the grid and the plurality of obstacles if the number of obstacles is plural;
determining a first cost of the mesh based on the minimum first distance and the second distance.
5. The method of claim 1, wherein determining a plurality of alternative destinations around the destination comprises:
generating a target graph with the destination as the center, wherein the range covered by the target graph is overlapped with the range covered by the grid area;
and sampling on the target graph, and taking a sampling point as the alternative destination.
6. The method of claim 1, wherein determining the second cost for each of the alternative destinations according to a preset second rule and the first cost of the grid comprises:
for each alternative destination, determining a first cost of a grid where the alternative destination is located;
determining a third distance between the alternate destination and the current location of the vehicle;
and determining a second cost of the alternative destination according to the first cost and the third distance.
7. The method of claim 1, further comprising:
determining invalid alternatives in the plurality of alternative destinations and screening out the invalid alternatives before judging whether a track curve between the alternative destinations and the current position of the vehicle meets a preset condition.
8. The method of claim 7, wherein determining invalid alternatives in the plurality of alternative destinations comprises:
for each alternative destination, determining whether the grid where the alternative destination is located collides with the obstacle;
if so, determining that the alternative destination is an invalid alternative.
9. The method according to any one of claims 1-8, further comprising:
if the track curves between all the alternative destinations and the current position of the vehicle do not meet the preset condition, determining a new alternative destination, and judging whether the track curve between the new alternative destination and the current position of the vehicle meets the preset condition or not according to the sequence of the second generation price of the new alternative destination from small to large.
10. The method of claim 9, further comprising:
determining whether the obstacle moves or not in the process that the automatic driving vehicle runs according to the running path;
if so, replanning the driving path of the vehicle according to the information of the moved obstacle.
11. A path planning apparatus, comprising:
an obstacle determination module to determine information of an obstacle between a current location of a vehicle and a destination of the vehicle;
the first calculation module is used for generating a grid area around the destination and determining a first cost of each grid in the grid area according to a preset first rule and the information of the obstacle; wherein the first price is used to indicate a feasibility of the grid;
a second calculation module, configured to determine a plurality of candidate destinations around the destination, and determine a second cost for each candidate destination according to a preset second rule and the first cost of the grid;
and the path determining module is used for sequentially judging whether the track curve between the alternative destination and the current position of the vehicle meets a preset condition according to the sequence of the second generation price from small to large until the track curve meeting the preset condition is determined, and taking the track curve meeting the preset condition as the running path of the vehicle.
12. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10.
13. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210640790.4A CN115060279B (en) | 2022-06-08 | 2022-06-08 | Path planning method, path planning device, electronic equipment and computer readable medium |
| PCT/CN2022/117365 WO2023236378A1 (en) | 2022-06-08 | 2022-09-06 | Path planning method and apparatus, and electronic device and computer-readable medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210640790.4A CN115060279B (en) | 2022-06-08 | 2022-06-08 | Path planning method, path planning device, electronic equipment and computer readable medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115060279A true CN115060279A (en) | 2022-09-16 |
| CN115060279B CN115060279B (en) | 2024-04-16 |
Family
ID=83200258
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210640790.4A Active CN115060279B (en) | 2022-06-08 | 2022-06-08 | Path planning method, path planning device, electronic equipment and computer readable medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115060279B (en) |
| WO (1) | WO2023236378A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117593900B (en) * | 2024-01-16 | 2024-05-14 | 浙江爱特电子技术股份有限公司 | Pre-hospital first-aid scheduling method and system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110153136A1 (en) * | 2009-12-17 | 2011-06-23 | Noel Wayne Anderson | System and method for area coverage using sector decomposition |
| JP5494845B1 (en) * | 2013-01-17 | 2014-05-21 | 株式会社デンソーアイティーラボラトリ | Information provision system |
| CN109712421A (en) * | 2019-02-22 | 2019-05-03 | 百度在线网络技术（北京）有限公司 | The speed planning method, apparatus and storage medium of automatic driving vehicle |
| CN110550029A (en) * | 2019-08-12 | 2019-12-10 | 华为技术有限公司 | obstacle avoiding method and device |
| US20200174475A1 (en) * | 2018-11-29 | 2020-06-04 | Electronics And Telecommunications Research Institute | Autonomous driving method and system |
| CN111857160A (en) * | 2020-08-17 | 2020-10-30 | 武汉中海庭数据技术有限公司 | Unmanned vehicle path planning method and device |
| CN112389427A (en) * | 2021-01-19 | 2021-02-23 | 腾讯科技（深圳）有限公司 | Vehicle track optimization method and device, electronic equipment and storage medium |
| CN112703144A (en) * | 2020-12-21 | 2021-04-23 | 华为技术有限公司 | Control method, related device and computer-readable storage medium |
| CN113238565A (en) * | 2021-06-16 | 2021-08-10 | 京东鲲鹏(江苏)科技有限公司 | Vehicle path planning method and device, vehicle and storage medium |
| CN113932823A (en) * | 2021-09-23 | 2022-01-14 | 同济大学 | Unmanned multi-target-point track parallel planning method based on semantic road map |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113515111B (en) * | 2020-03-25 | 2023-08-25 | 宇通客车股份有限公司 | Vehicle obstacle avoidance path planning method and device |
| US11498586B2 (en) * | 2020-03-27 | 2022-11-15 | Wipro Limited | Method and system for dynamically generating a secure navigation path for navigation of autonomous vehicle |
| CN113267199B (en) * | 2021-06-24 | 2023-01-06 | 上海欧菲智能车联科技有限公司 | Method and device for planning driving track |
| CN113819917A (en) * | 2021-09-16 | 2021-12-21 | 广西综合交通大数据研究院 | Automatic driving path planning method, device, equipment and storage medium |
| CN114506343B (en) * | 2022-03-02 | 2024-07-16 | 阿波罗智能技术(北京)有限公司 | Track planning method, device, equipment, storage medium and automatic driving vehicle |
-
2022
- 2022-06-08 CN CN202210640790.4A patent/CN115060279B/en active Active
- 2022-09-06 WO PCT/CN2022/117365 patent/WO2023236378A1/en unknown
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110153136A1 (en) * | 2009-12-17 | 2011-06-23 | Noel Wayne Anderson | System and method for area coverage using sector decomposition |
| JP5494845B1 (en) * | 2013-01-17 | 2014-05-21 | 株式会社デンソーアイティーラボラトリ | Information provision system |
| US20200174475A1 (en) * | 2018-11-29 | 2020-06-04 | Electronics And Telecommunications Research Institute | Autonomous driving method and system |
| CN109712421A (en) * | 2019-02-22 | 2019-05-03 | 百度在线网络技术（北京）有限公司 | The speed planning method, apparatus and storage medium of automatic driving vehicle |
| CN110550029A (en) * | 2019-08-12 | 2019-12-10 | 华为技术有限公司 | obstacle avoiding method and device |
| CN111857160A (en) * | 2020-08-17 | 2020-10-30 | 武汉中海庭数据技术有限公司 | Unmanned vehicle path planning method and device |
| CN112703144A (en) * | 2020-12-21 | 2021-04-23 | 华为技术有限公司 | Control method, related device and computer-readable storage medium |
| CN112389427A (en) * | 2021-01-19 | 2021-02-23 | 腾讯科技（深圳）有限公司 | Vehicle track optimization method and device, electronic equipment and storage medium |
| CN113238565A (en) * | 2021-06-16 | 2021-08-10 | 京东鲲鹏(江苏)科技有限公司 | Vehicle path planning method and device, vehicle and storage medium |
| CN113932823A (en) * | 2021-09-23 | 2022-01-14 | 同济大学 | Unmanned multi-target-point track parallel planning method based on semantic road map |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023236378A1 (en) | 2023-12-14 |
| CN115060279B (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111301409A (en) | Parking path planning method and device, vehicle and storage medium | |
| US20170341641A1 (en) | Vehicle collision avoidance | |
| US11518381B2 (en) | Enhanced threat selection | |
| CN112418092B (en) | Fusion method, device, equipment and storage medium for obstacle perception | |
| JP2015081083A (en) | Confidence estimation for predictive driver assistance systems based on plausibility rules | |
| JP2002168953A (en) | Device for monitoring vicinity of vehicle | |
| CN111813101A (en) | Robot path planning method and device, terminal equipment and storage medium | |
| CN112362065B (en) | Obstacle detouring track planning method and device, storage medium, control unit and equipment | |
| EP3842316B1 (en) | Method and device for controlling operation of self-driving car | |
| US20230091505A1 (en) | Parking assistance device, parking assistance method, and computer program product | |
| CN111707258B (en) | External vehicle monitoring method, device, equipment and storage medium | |
| CN115060279B (en) | Path planning method, path planning device, electronic equipment and computer readable medium | |
| US11383704B2 (en) | Enhanced vehicle operation | |
| CN110696826A (en) | Method and device for controlling a vehicle | |
| CN113753040B (en) | Predicting road crossing behavior of vulnerable road users | |
| CN114895250A (en) | Radar data processing method and device, electronic equipment and storage medium | |
| US20220333933A1 (en) | Enhanced vehicle and trailer operation | |
| CN110648547A (en) | Transport infrastructure communication and control | |
| US20240140488A1 (en) | Autonomous driving control apparatus and method for generating route thereof | |
| US11468767B2 (en) | Map information system | |
| WO2023185255A1 (en) | Collision early warning method, vehicle-mounted terminal and storage medium | |
| CN116699596A (en) | Method and device for correcting speed of millimeter wave radar of vehicle | |
| CN115817466A (en) | Collision risk assessment method and device | |
| CN112183157A (en) | Road geometry identification method and device | |
| US20220063671A1 (en) | Vehicle operation along planned path |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 314500 988 Tong Tong Road, Wu Tong Street, Tongxiang, Jiaxing, Zhejiang Applicant after: United New Energy Automobile Co.,Ltd. Address before: 314500 988 Tong Tong Road, Wu Tong Street, Tongxiang, Jiaxing, Zhejiang Applicant before: Hozon New Energy Automobile Co., Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |