CN110929702A - Trajectory planning method and device, electronic equipment and storage medium - Google Patents
Trajectory planning method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN110929702A CN110929702A CN202010072876.2A CN202010072876A CN110929702A CN 110929702 A CN110929702 A CN 110929702A CN 202010072876 A CN202010072876 A CN 202010072876A CN 110929702 A CN110929702 A CN 110929702A
- Authority
- CN
- China
- Prior art keywords
- track
- target
- obstacle information
- trajectory
- initial
- 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 45
- 238000007621 cluster analysis Methods 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims description 36
- 230000015654 memory Effects 0.000 claims description 19
- 238000009499 grossing Methods 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000007499 fusion processing Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 12
- 238000010845 search algorithm Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 239000004973 liquid crystal related substance Substances 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 101150062705 Wipf3 gene Proteins 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010924 continuous production Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001151 other effect Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 102220040233 rs79219465 Human genes 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The application discloses a track planning method, a track planning device, electronic equipment and a storage medium, and relates to the field of automatic driving. The method and the device can be applied to the field of automatic driving, in particular to the field of automatic parking. The specific implementation scheme is as follows: performing cluster analysis on the obstacle information around the target parking space to determine first obstacle information and second obstacle information; determining an initial track midpoint of a target vehicle entering a target parking space according to the first obstacle information; generating a first track between the target position and the midpoint of the initial track according to the target position of the target vehicle in the target parking space, the midpoint of the initial track and the first obstacle information; generating a second track between the current position and the midpoint of the initial track according to the current position of the target vehicle, the midpoint of the initial track and the information of the second obstacle; and controlling the target vehicle to reach the target position according to the first track and the second track. According to the technical scheme of the embodiment of the application, the consumption of computing resources can be reduced, and the speed of trajectory planning is increased.
Description
Technical Field
The present application relates to the field of autopilot, and more particularly to the field of trajectory planning.
Background
The automatic driving technology is a technology which enables a computer to automatically and safely operate a vehicle by means of cooperation of artificial intelligence, visual calculation, radar, a monitoring device and a global positioning system. At present, a search algorithm is basically adopted to search a global path in automatic driving to realize track planning, and the problems of high consumption of computing resources and low computing speed exist.
Disclosure of Invention
The embodiment of the invention provides a trajectory planning method, a trajectory planning device, electronic equipment and a storage medium, and aims to solve one or more technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a trajectory planning method, including:
performing cluster analysis on the obstacle information around the target parking space to determine first obstacle information and second obstacle information; the first obstacle information is used for indicating obstacle information located at the near end of a target parking space, and the second obstacle information is used for indicating obstacle information located at the far end of the target parking space;
determining the midpoint of an initial track of a target vehicle entering the target parking space according to the first obstacle information;
generating a first track between a target position and a midpoint of the initial track according to the target position of the target vehicle in the target parking space, the midpoint of the initial track and the first obstacle information;
generating a second track between the current position and the initial track midpoint according to the current position of the target vehicle, the initial track midpoint and the second obstacle information;
and controlling the target vehicle to reach the target position according to the first track and the second track.
In one embodiment, generating a second trajectory between the current position and the initial trajectory midpoint based on the current position of the target vehicle, the initial trajectory midpoint, and the second obstacle information includes:
and generating the second track by adopting a track point searching algorithm according to the second obstacle information.
In one embodiment, generating a first track between the target position and the initial track midpoint according to the target position of the target vehicle in the target parking space, the initial track midpoint and the first obstacle information includes:
planning a path between the initial parking track starting point and the target position through a geometric algorithm to generate a first preselected track;
and smoothing the first preselected trajectory through a CC curve algorithm to form the first trajectory.
In one embodiment, controlling the target vehicle to reach the target location based on the first trajectory and the second trajectory comprises:
fusing the first track and the second track to generate a parking track of the target vehicle reaching the target position;
and controlling the target vehicle to reach the target position according to the parking track.
In one embodiment, the determining the initial trajectory midpoint of the target vehicle into the target parking space according to the first obstacle information includes:
sequentially determining a plurality of non-obstacle areas according to a priority order from long to short of the plurality of shortest distances;
searching for a position point within the non-obstacle region that can reach the target position each time the non-obstacle region is determined;
and in response to the searching of the position point, setting the position point as the middle point of the initial track and stopping searching.
In one embodiment, the cluster analysis of the obstacle information around the target parking space includes:
determining an initial planned trajectory for the target vehicle from the current location to the target location;
detecting whether the target vehicle has collision risk according to the initial planning track;
and responding to the detected collision risk of the target vehicle, and carrying out cluster analysis on the obstacle information around the target parking space.
In a second aspect, an embodiment of the present invention provides a trajectory planning apparatus, including:
the first determining module is used for carrying out clustering analysis on the obstacle information around the target parking space so as to determine first obstacle information and second obstacle information; the first obstacle information is used for indicating obstacle information located at the near end of a target parking space, and the second obstacle information is used for indicating obstacle information located at the far end of the target parking space;
the second determining module is used for determining the midpoint of the initial track of the target vehicle entering the target parking space according to the first obstacle information;
the first generation module is used for generating a first track between a target position and a midpoint of the initial track according to the target position of the target vehicle in the target parking space, the midpoint of the initial track and the first obstacle information;
a second generation module, configured to generate a second track between the current position and the initial track midpoint according to the current position of the target vehicle, the initial track midpoint, and the second obstacle information;
and the control module is used for controlling the target vehicle to reach the target position according to the first track and the second track.
In one embodiment, the second generating module comprises:
and the second generating submodule is used for generating the second track by adopting a track point searching algorithm according to the second obstacle information.
In one embodiment, the first generating module comprises:
the first generation submodule is used for planning a path between the initial parking track starting point and the target position through a geometric algorithm to generate a first preselected track;
and the smoothing sub-module is used for smoothing the first preselected trajectory through a CC curve algorithm to form the first trajectory.
In one embodiment, the control module comprises:
the fusion processing submodule is used for fusing the first track and the second track to generate a parking track of the target vehicle reaching the target position;
and the vehicle control sub-module is used for controlling the target vehicle to reach the target position according to the parking track.
In one embodiment, the first obstacle information includes a shortest distance between the obstacle corresponding to the first obstacle information and the target parking space, and the second determining module includes:
a second determination submodule configured to sequentially determine a plurality of non-obstacle areas in a priority order of the plurality of shortest distances from long to short;
a search sub-module for searching for a position point within the non-obstacle area that can reach the target position each time the non-obstacle area is determined;
and the setting submodule is used for setting the position point as the midpoint of the initial track and stopping searching in response to the position point being searched.
In one embodiment, the first determining module comprises:
a first determination submodule for determining an initial planned trajectory for the target vehicle from the current position to the target position;
the detection submodule is used for detecting whether the target vehicle has collision risks or not according to the initial planning track;
and the cluster analysis submodule is used for responding to the detected collision risk of the target vehicle and carrying out cluster analysis on the obstacle information around the target parking space.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the trajectory planning methods described above.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any one of the trajectory planning methods described above.
One embodiment in the above application has the following advantages or benefits: the method has the advantages that the method adopts clustering analysis to divide the obstacle information around the target parking space into the first obstacle information and the second obstacle information, so that the extraction of necessary obstacle information is realized, the unnecessary obstacle information can be prevented from participating in the calculation of trajectory planning, and the calculation amount is reduced; in addition, the first track and the second track are generated in a segmented mode by determining the midpoint of the initial track, so that the traversal times in traversal operation can be reduced, the technical problems of high consumption of computing resources and low computing speed caused by the fact that a search algorithm is adopted to carry out global track planning are solved, and the technical effects of reducing the consumption of the computing resources and improving the computing speed and the track planning speed are achieved.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram of an application scenario according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram of a trajectory planning method according to a first embodiment of the present application;
FIG. 3 is a schematic illustration of a second trajectory generated according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating a first track generation method according to an implementation of the first embodiment of the present application;
FIG. 5 is a schematic illustration of a first track generated according to an embodiment of the present application;
FIG. 6 is a flowchart illustrating a control method for a target vehicle to reach a target parking space according to an embodiment of the first embodiment of the present application;
FIG. 7A is a schematic illustration of a parking trajectory generated in accordance with an embodiment of the present application;
FIG. 7B is a schematic illustration of another parking trajectory generated in accordance with an embodiment of the present application;
FIG. 7C is a schematic illustration of yet another parking trajectory generated in accordance with an embodiment of the present application;
FIG. 8 is a schematic flow chart diagram illustrating an initial midpoint placement pattern in a trace according to one implementation of the first embodiment of the present application;
FIG. 9 is a schematic flow chart of a collision detection scheme according to one implementation of a first example of the present application;
FIG. 10 is a block diagram of a trajectory planning device according to a second embodiment of the present application;
FIG. 11 is a block diagram of a trajectory planning device according to an embodiment of a second embodiment of the present application;
FIG. 12 is a block diagram of a trajectory planning device according to another embodiment of the second embodiment of the present application;
FIG. 13 is a block diagram of a trajectory planning device according to yet another embodiment of the second embodiment of the present application;
FIG. 14 is a block diagram of a trajectory planning device according to yet another embodiment of the second embodiment of the present application;
FIG. 15 is a block diagram of a trajectory planning device according to a further embodiment of the second embodiment of the present application;
fig. 16 is a block diagram of an electronic device for implementing a trajectory planning method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the field of autonomous driving, the planned trajectory between the current position of the target vehicle and the target position is basically generated using a search algorithm. Because the obstacle information between the current position of the target vehicle and the target position is complex, the calculation amount of the traversal calculation in the search algorithm is huge, and the problems of high calculation resource consumption and low calculation speed exist when the search algorithm is adopted for carrying out the global path search.
The trajectory planning method, the trajectory planning device, the electronic equipment and the storage medium in the embodiment of the application can be applied to a scene with obstacles around a target parking space and a scene without obstacles around the target parking space, wherein the target parking space can be an outdoor parking space, an indoor parking space, a garage, a parking space projected temporarily and the like.
For example, the embodiment of the application can be applied to a scene shown in fig. 1 where obstacles are located around the target parking space. In fig. 1, a matrix of solid circles may represent an obstacle, a rectangle with an opening formed by the solid circles may represent the target space 100, a point a may represent an initial trajectory midpoint, a point B may represent a target position of the target vehicle in the target space, and a point C may represent a current position of the target vehicle.
Fig. 2 is a schematic flow chart of a trajectory planning method according to a first embodiment of the present application. As shown in fig. 2, the method may include the steps of:
step S201, performing cluster analysis on obstacle information around a target parking space to determine first obstacle information and second obstacle information; the first obstacle information is used for indicating the obstacle information located at the near end of the target parking space, and the second obstacle information is used for indicating the obstacle information located at the far end of the target parking space.
The obstacle information can be acquired through a sensor, road side equipment, a cloud server, a grid map and the like. For example, the obstacle information may be that when the target vehicle travels to the periphery of the target parking space, the vehicle-mounted sensor of the target vehicle detects the obstacle information, roadside devices arranged around the target parking space detect the obstacle information, the obstacle information is detected in real time by the sensor and stored in the cloud server, a grid map formed by modeling the periphery of the target parking space is formed, and the like.
The obstacle information may include the speed, size, location of the obstacle and the distance between the obstacle and the target slot. The obstacle may be an object that obstructs the travel of the target vehicle, such as a pedestrian, an animal, a building, or another vehicle other than the target vehicle, and the present application does not specifically limit the type of the obstacle, and the object may be an object that obstructs the travel of the target vehicle, and the other vehicle may be a manually driven vehicle or an automatically driven vehicle.
In one example, as shown in fig. 1, the obstacle information may be subjected to cluster analysis according to the distance between the obstacle and the target parking space 100, so as to partition the first obstacle information and the second obstacle information, thereby extracting necessary information required by trajectory planning, avoiding the unnecessary obstacle information around the target parking space from participating in the trajectory planning operation, reducing the operation amount of trajectory planning, and increasing the calculation speed. The obstacle corresponding to the first obstacle information is an obstacle 101 located at the near end of the target parking space, and the obstacle corresponding to the second obstacle information is an obstacle 102 located at the far end of the target parking space.
And S202, determining the middle point of the initial track of the target vehicle entering the target parking space according to the first obstacle information.
In one example, as shown in fig. 3, the initial trajectory midpoint a is used to indicate the initially planned trajectory midpoint in the trajectory for the target vehicle to reach the target location B from the current location C.
Step S203, generating a first track between the target position and the initial track midpoint according to the target position of the target vehicle in the target parking space, the initial track midpoint and the first obstacle information.
In one example, the number of segments of the generated first track may be a continuous track or a multi-segment track. The number of the sections of the first track can be selected and adjusted according to the distribution condition of the obstacles near the target parking space, and the number of the generation sections of the first track is not limited by the application.
Since the first obstacle information is the obstacle information of the near end of the target parking space, and the obstacle near the parking space is usually a fixed obstacle, the first obstacle information is usually static data in a period of time (such as planning time), and the first trajectory can be planned by using a simple geometric algorithm. In practical experience, the first trajectory is usually a trajectory for parallel parking (side parking) or a trajectory for vertical parking.
And step S204, generating a second track between the current position and the initial track midpoint according to the current position of the target vehicle, the initial track midpoint and the second obstacle information.
In one example, the generated second track may be a continuous track or a multi-segment track. The number of the second track sections can be selected and adjusted according to the distribution condition of the obstacles at the far end of the target parking space, and the number of the second track generation sections is not limited in the application.
And S205, controlling the target vehicle to reach the target position according to the first track and the second track.
In the embodiment of the application, the first obstacle information and the second obstacle information are determined by performing cluster analysis on the obstacles around the target parking space, so that the extraction of necessary track planning information can be realized, unnecessary obstacle information can be prevented from participating in track planning operation, and the operation amount is reduced. In addition, according to different characteristics of the near-end obstacle and the far-end obstacle of the target parking space, the planning track is formed by generating the first track and the second track, different algorithms can be selected in a targeted mode in the planning process, and the calculation speed and the planning efficiency can be improved.
In one embodiment, step S204 may include: and generating a second track by adopting a track point searching algorithm according to the second obstacle information.
In one example, as shown in fig. 3, a search area 301 may be determined according to the current position of the target vehicle, the midpoint of the initial trajectory, and the second obstacle information, so that the obstacle 102 located at the far end of the target parking space and corresponding to the second obstacle information is located outside the search area 301, thereby avoiding performing traversal search on the second obstacle information, reducing consumption of computing resources, and further improving computing speed. Further, a track point search algorithm is adopted to search for an optimal track between the current position of the target vehicle and the midpoint of the initial track in the search area 301 to form a second track CA based on discrete track points. The track point searching algorithm can be an A star algorithm, a mixed A star algorithm and the like, and the specific track point searching algorithm is not limited as long as track point searching can be realized.
The second obstacle information is information of an obstacle at the far end of the target parking space, and the far end of the target parking space generally has complex road conditions and complex and variable obstacles, so the second obstacle information is generally dynamic data. The track point algorithm is used as a dynamic search algorithm, and is more suitable for calculation and planning in an unknown environment so as to improve the accuracy of planning.
In one embodiment, as shown in fig. 4, step S203 may include:
s401, planning a path between the midpoint of the initial track and the target position through a geometric algorithm to generate a first preselected track;
in one example, the size of the obstacle includes a circular obstacle area of the obstacle, which is used to indicate the smallest circle that can cover the obstacle, and which must be bypassed when the target vehicle enters the range of the circular obstacle area. Step S401 may include: setting an initial straight line track between a target position and an initial track midpoint according to the target position of a target vehicle in a target parking space and the initial track midpoint; and generating a first preselected track according to the initial straight-line track and the circular obstacle area of the obstacle corresponding to the first obstacle information so as to form an optimal track of the target vehicle capable of bypassing the obstacle corresponding to the first obstacle information.
As described above, the first obstacle information is usually static data, and thus the first trajectory can be planned using a simple geometric algorithm, which can reduce the amount of calculation and increase the planning speed.
And S402, smoothing the first preselected trajectory through a CC curve algorithm to form a first trajectory.
In one example, as shown in fig. 5, there may be a polyline track in the first preselected track generated by a geometric algorithm, and the polyline track in the first preselected track may be smoothed by a CC (Curvature continuity) curve algorithm to form a smoothed first track AC. The CC curve algorithm may be a curvature continuous spline curve algorithm such as a bezier curve (bezier curve) algorithm and a B spline curve (beziershift curve) algorithm, and the application does not limit the specific type of the CC curve algorithm, as long as the first preselected trajectory can be smoothed.
In the above embodiment, since the first preselected trajectory is a trajectory based on geometric features generated by a geometric algorithm, and the operation amount of the first preselected trajectory is much smaller than the traversal operation in the search algorithm, and meanwhile, the smoothing processing of the first preselected trajectory is also completed by a CC curve algorithm based on geometric features, and the operation amount is also smaller, the operation amount for generating the first trajectory can be effectively reduced, the calculation resources are saved, the calculation speed is increased, and the trajectory planning speed can be effectively increased.
In one embodiment, as shown in fig. 6, step S205 may include:
s601, fusing the first track and the second track to generate a parking track when the target vehicle reaches the target position;
in one example, as shown in fig. 3, since the second track may be discrete track points generated using a track point search algorithm, there may be a discontinuity between the second track and the midpoint of the initial track. In this way, when the target vehicle travels to the midpoint of the initial trajectory along the second trajectory, the midpoint of the initial trajectory may not be reached, the target vehicle may not travel to the target position along the first trajectory from the midpoint of the initial trajectory accurately, and the accuracy of trajectory planning is low. For example, when the sampling precision of the track point search algorithm is 0.2m, a gap of 0.2m may exist between the end discrete track point close to the middle point of the initial track and the middle point of the initial track in the second track, and the target vehicle cannot be driven from the second track to the middle point of the initial track.
In view of this, as shown in fig. 3, fig. 5 and fig. 7A, in step S601, the first trajectory CA and the second trajectory AB may be subjected to a continuous process by using a trajectory fusion algorithm, so as to generate a complete and continuous parking trajectory C1A1B that meets the vehicle kinematics and allows the target vehicle to reach the target position, which may effectively improve the accuracy of trajectory planning. The intersection point of the first continuous track C1A1 and the second continuous track A1B is a final track midpoint A1, the target vehicle can accurately drive from the continuous current position C1 to the continuous track midpoint A1 and drive from the track midpoint A1 to the target position B along the continuous first track A1B, the precision of track planning can be effectively improved, and the accuracy of the target vehicle entering the target parking space is improved. As in fig. 7A, the broken line rectangle indicates that the target vehicle is located at the current position C1 after the serialization, and the solid line rectangle indicates that the target vehicle is located at the target position B.
In one example, the first trajectory C1A1 and the second trajectory A1B may be generated simultaneously, that is, the first trajectory C1A1 and the second trajectory A1B may be generated in parallel by the method of any of the above embodiments, and then the final trajectory midpoint A1 is determined by the intersection point of the first trajectory C1A1 and the second trajectory A1B.
It should be noted that the first trajectory C1a1 that is made continuous in the embodiment of the present application is not limited to one trajectory, and may be made continuous in at least two trajectories. For example, as shown in FIG. 7B, during parallel parking, the serialized first trajectory C1A1 may be a serialization of first parallel parking trajectory segments C1C11, C11C12, and C12A 1; as shown in fig. 7C, during vertical parking, the serialized first trajectory C1a1 may be a serialization of first vertical parking trajectory segments C1C21 and C21a 1. The continuous second trajectory A1B is not limited to only one trajectory, but may be a continuous trajectory of at least two trajectories, which is not limited in the embodiment of the present application. For example, as shown in FIG. 7B, during parallel parking, the serialized second trajectory A1B may be a serialization of second parallel parking trajectory segments A1A12, A12A13, and A13B; as shown in fig. 7C, during vertical parking, the second serialized trajectory A1B may be a serialization of the second vertical parking trajectory segments A1a21 and a 21B.
And S602, controlling the target vehicle to reach the target position according to the parking track.
In one example, after the current position of the target vehicle, the target position in the target parking space, and the parking trajectory are determined, vehicle driving parameters such as a driving speed and a wheel rotation angle of the target vehicle may be controlled to enable the target vehicle to reach the target position along the parking trajectory.
In one example, a trajectory tracking algorithm may also be used to control the target vehicle to reach the target location based on the parking trajectory. The control method for the target vehicle to reach the target position is not limited, and the control method for the target vehicle to reach the target position according to the parking track can be realized.
In one embodiment, the first obstacle information includes a shortest distance between an obstacle corresponding to the first obstacle information and the target parking space, as shown in fig. 8, step S202 may include:
step S801, sequentially determining a plurality of non-obstacle areas according to a priority order of a plurality of shortest distances from long to short;
step S802, when the non-obstacle area is determined each time, judging that a position point which can reach the target position is searched in the non-obstacle area;
and step S803, in response to the position point being searched, setting the position point as the middle point of the initial track and stopping the search.
For example, as shown in fig. 1, the obstacle 101 located at the near end of the target parking space includes a first near-end obstacle 801 and a second near-end obstacle 802, and the distance between the first near-end obstacle 801 and the target parking space 100 is smaller than the distance between the second near-end obstacle 802 and the target parking space 100, it is determined whether a non-obstacle area exists in the area between the first near-end obstacle 801 and the target parking space 100; if no non-obstacle zone is present, then a further determination is made as to whether a non-obstacle zone is present in the area between second proximate obstacle 802 and target vehicle space 100. Thus, the position points can be determined according to the priority sequence from near to far from the target parking space 100, so that the optimal track can be generated quickly, and the speed of track planning is increased.
It is understood that the determination range of the non-obstacle area may be expanded when there is no non-obstacle area in the area between the first near-end obstacle 801 and the target vehicle space 100, and in the area between the second near-end obstacle 802 and the target vehicle space 100.
In an embodiment, as shown in fig. 9, the clustering analysis of the obstacle information around the target parking space in step S201 may include:
step S901, determining an initial planned track of a target vehicle from a current position to a target position;
step S902, detecting whether the target vehicle has collision risk according to the initial planning track;
and step S903, responding to the detected collision risk of the target vehicle, and performing cluster analysis on the obstacle information around the target parking space.
In one example, the initial planned trajectory may be set according to a positional relationship between a current position of the target vehicle and a target position in the target parking space, and the target vehicle may first travel according to the initial planned trajectory without considering information of obstacles around the target parking space, thereby reducing an amount of computation for generating the first trajectory and the second trajectory, and increasing a computation speed of trajectory planning.
Optionally, the initial planned trajectory may be set according to a principle that a distance between the current position of the target vehicle and the target position in the target parking space is shortest. And when the target vehicle is detected to have no collision risk according to the initial planning track, the target vehicle runs along the initial planning track to reach the target position.
In the embodiment, when the collision risk of the target vehicle is detected according to the initial planned track, the cluster analysis of the obstacle information around the target is triggered to plan the track, so that the speed and efficiency of the track planning can be effectively improved.
Fig. 10 is a block diagram of a trajectory planning device according to a second embodiment of the present application. As shown in fig. 10, the trajectory planning apparatus 1000 includes:
the first determining module 1001 may be configured to perform cluster analysis on obstacle information around a target parking space to determine first obstacle information and second obstacle information; the first obstacle information is used for indicating the obstacle information positioned at the near end of the target parking space, and the second obstacle information is used for indicating the obstacle information positioned at the far end of the target parking space; the second determining module 1002 may be configured to determine, according to the first obstacle information, an initial trajectory midpoint of the target vehicle entering the target parking space; the first generating module 1003 may be configured to generate a first track between a target position and an initial track midpoint according to the target position of the target vehicle in the target parking space, the initial track midpoint, and the first obstacle information; the second generating module 1004 may be configured to generate a second track between the current position and the initial track midpoint according to the current position of the target vehicle, the initial track midpoint, and the second obstacle information; the control module 1005 may be configured to control the target vehicle to reach the target location based on the first trajectory and the second trajectory.
In one embodiment, as shown in fig. 11, the second generation module 1004 may include a second generation submodule 1101. The second generating sub-module 1101 may be configured to generate a second track by using a track point search algorithm according to the second obstacle information.
In one embodiment, as shown in fig. 12, the first generation module 1003 may include a first generation sub-module 1201 and a smoothing sub-module 1202. The first generation submodule 1201 may be configured to plan a path between an initial parking trajectory starting point and a target position through a geometric algorithm, and generate a first preselected trajectory; the smoothing sub-module 1202 may be configured to smooth the first preselected trajectory with the CC curve algorithm to form a first trajectory.
In one embodiment, as shown in FIG. 13, the control module 1005 may include a fusion process sub-module 1301 and a vehicle control sub-module 1302. The fusion processing submodule 1301 may be configured to fuse the first trajectory and the second trajectory to generate a parking trajectory of the target vehicle reaching the target position; the vehicle control sub-module 1302 may be configured to control the target vehicle to reach the target location based on the parking trajectory.
In one embodiment, the first obstacle information may include a shortest distance between an obstacle corresponding to the first obstacle information and the target parking space, as shown in fig. 14, and the second determining module 1002 may include a second determining submodule 1401, a searching submodule 1402 and a setting submodule 1403. The second determination submodule 1401 may be configured to sequentially determine the plurality of non-obstacle areas in order of priority from long to short of the plurality of shortest distances; the search sub-module 1402 may be configured to search for a location point within the non-obstacle area that can reach the target location each time the non-obstacle area is determined; the setting sub-module 1403 may be used to set the location point to the initial midpoint of the trajectory and stop the search in response to the location point being searched.
In one embodiment, as shown in fig. 15, the first determination module 1001 may include a first determination sub-module 1501, a detection sub-module 1502, and a cluster analysis sub-module 1503. The first determination submodule 1501 may be configured to determine an initial planned trajectory of the target vehicle from the current position to the target position; the detection submodule 1502 may be configured to detect whether the target vehicle is at risk of collision according to the initial planned trajectory; the cluster analysis submodule 1503 may be configured to perform cluster analysis on the obstacle information around the target parking space in response to detecting that the target vehicle has a collision risk.
The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 16 is a block diagram of an electronic device according to the trajectory planning method of the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 16, the electronic apparatus includes: one or more processors 1601, memory 1602, and interfaces for connecting components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display Graphical information for a Graphical User Interface (GUI) on an external input/output device, such as a display device coupled to the Interface. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 16 illustrates an example of a processor 1601.
The memory 1602, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the trajectory planning method in the embodiment of the present application (for example, the first determining module 1001, the second determining module 1002, the first generating module 1003, the second generating module 1004, and the control module 1005 shown in fig. 10). The processor 1601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 1602, so as to implement the trajectory planning method in the above-described method embodiments.
The memory 1602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the electronic device of the trajectory planning method, and the like. Further, the memory 1602 may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 1602 may optionally include memory located remotely from the processor 1601, which may be connected to the trajectory planning method electronics via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the trajectory planning method may further include: an input device 1603 and an output device 1604. The processor 1601, the memory 1602, the input device 1603, and the output device 1604 may be connected by a bus or other means, which is exemplified in fig. 16.
The input device 1603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic equipment of the trajectory planning method, e.g. a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer, one or more mouse buttons, a track ball, a joystick or other input devices. The output devices 1604 may include a display device, auxiliary lighting devices (e.g., LEDs), tactile feedback devices (e.g., vibrating motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD) such as a Liquid crystal Cr16 star display 16, a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, Integrated circuitry, Application Specific Integrated Circuits (ASICs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (Cathode ray Tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (14)
1. A trajectory planning method, comprising:
performing cluster analysis on the obstacle information around the target parking space to determine first obstacle information and second obstacle information; the first obstacle information is used for indicating obstacle information located at the near end of a target parking space, and the second obstacle information is used for indicating obstacle information located at the far end of the target parking space;
determining the midpoint of an initial track of a target vehicle entering the target parking space according to the first obstacle information;
generating a first track between a target position and a midpoint of the initial track according to the target position of the target vehicle in the target parking space, the midpoint of the initial track and the first obstacle information;
generating a second track between the current position and the initial track midpoint according to the current position of the target vehicle, the initial track midpoint and the second obstacle information;
and controlling the target vehicle to reach the target position according to the first track and the second track.
2. The method of claim 1, wherein generating a second trajectory between the current position and the initial trajectory midpoint based on the current position of the target vehicle, the initial trajectory midpoint, and the second obstacle information comprises:
and generating the second track by adopting a track point searching algorithm according to the second obstacle information.
3. The method of claim 1, wherein generating a first trajectory between the target position and the initial trajectory midpoint based on the target position of the target vehicle in the target slot, the initial trajectory midpoint, and the first obstacle information comprises:
planning a path between the midpoint of the initial trajectory and the target position through a geometric algorithm to generate a first preselected trajectory;
and smoothing the first preselected trajectory through a CC curve algorithm to form the first trajectory.
4. The method of claim 1, wherein controlling the target vehicle to reach the target location based on the first trajectory and the second trajectory comprises:
fusing the first track and the second track to generate a parking track of the target vehicle reaching the target position;
and controlling the target vehicle to reach the target position according to the parking track.
5. The method of claim 1, wherein the first obstacle information comprises a shortest distance between the obstacle corresponding to the first obstacle information and the target parking space, and wherein determining an initial trajectory midpoint of the target vehicle into the target parking space according to the first obstacle information comprises:
sequentially determining a plurality of non-obstacle areas according to a priority order from long to short of the plurality of shortest distances;
searching for a position point within the non-obstacle region that can reach the target position each time the non-obstacle region is determined;
and in response to the searching of the position point, setting the position point as the middle point of the initial track and stopping searching.
6. The method of claim 1, wherein the cluster analysis of the obstacle information around the target parking space comprises:
determining an initial planned trajectory for the target vehicle from the current location to the target location;
detecting whether the target vehicle has collision risk according to the initial planning track;
and responding to the detected collision risk of the target vehicle, and carrying out cluster analysis on the obstacle information around the target parking space.
7. A trajectory planning apparatus, comprising:
the first determining module is used for carrying out clustering analysis on the obstacle information around the target parking space so as to determine first obstacle information and second obstacle information; the first obstacle information is used for indicating obstacle information located at the near end of a target parking space, and the second obstacle information is used for indicating obstacle information located at the far end of the target parking space;
the second determining module is used for determining the midpoint of the initial track of the target vehicle entering the target parking space according to the first obstacle information;
the first generation module is used for generating a first track between a target position and a midpoint of the initial track according to the target position of the target vehicle in the target parking space, the midpoint of the initial track and the first obstacle information;
a second generation module, configured to generate a second track between the current position and the initial track midpoint according to the current position of the target vehicle, the initial track midpoint, and the second obstacle information;
and the control module is used for controlling the target vehicle to reach the target position according to the first track and the second track.
8. The apparatus of claim 7, wherein the second generating module comprises:
and the second generating submodule is used for generating the second track by adopting a track point searching algorithm according to the second obstacle information.
9. The apparatus of claim 7, wherein the first generating module comprises:
the first generation submodule is used for planning a path between the initial parking track starting point and the target position through a geometric algorithm to generate a first preselected track;
and the smoothing sub-module is used for smoothing the first preselected trajectory through a CC curve algorithm to form the first trajectory.
10. The apparatus of claim 7, wherein the control module comprises:
the fusion processing submodule is used for fusing the first track and the second track to generate a parking track of the target vehicle reaching the target position;
and the vehicle control sub-module is used for controlling the target vehicle to reach the target position according to the parking track.
11. The apparatus of claim 7, wherein the first obstacle information comprises a shortest distance between the obstacle corresponding to the first obstacle information and the target space, and the second determining module comprises:
a second determination submodule configured to sequentially determine a plurality of non-obstacle areas in a priority order of the plurality of shortest distances from long to short;
a search sub-module for searching for a position point within the non-obstacle area that can reach the target position each time the non-obstacle area is determined;
and the setting submodule is used for setting the position point as the midpoint of the initial track and stopping searching in response to the position point being searched.
12. The apparatus of claim 7, wherein the first determining module comprises:
a first determination submodule for determining an initial planned trajectory for the target vehicle from the current position to the target position;
the detection submodule is used for detecting whether the target vehicle has collision risks or not according to the initial planning track;
and the cluster analysis submodule is used for responding to the detected collision risk of the target vehicle and carrying out cluster analysis on the obstacle information around the target parking space.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010072876.2A CN110929702B (en) | 2020-01-22 | 2020-01-22 | Trajectory planning method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010072876.2A CN110929702B (en) | 2020-01-22 | 2020-01-22 | Trajectory planning method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110929702A true CN110929702A (en) | 2020-03-27 |
| CN110929702B CN110929702B (en) | 2020-05-08 |
Family
ID=69854417
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010072876.2A Active CN110929702B (en) | 2020-01-22 | 2020-01-22 | Trajectory planning method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110929702B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111752271A (en) * | 2020-04-16 | 2020-10-09 | 北京京东乾石科技有限公司 | Trajectory prediction method, apparatus, device and storage medium |
| CN111879330A (en) * | 2020-08-05 | 2020-11-03 | 苏州挚途科技有限公司 | Method and device for planning crossing driving path, processor and automatic driving vehicle |
| CN112277930A (en) * | 2020-10-20 | 2021-01-29 | 上海欧菲智能车联科技有限公司 | Horizontal autonomous parking control method and device, vehicle and storage medium |
| TWI737437B (en) * | 2020-08-07 | 2021-08-21 | 財團法人車輛研究測試中心 | Trajectory determination method |
| CN113401141A (en) * | 2021-07-12 | 2021-09-17 | 北京百度网讯科技有限公司 | Route processing method and device |
| CN113734190A (en) * | 2021-09-09 | 2021-12-03 | 北京百度网讯科技有限公司 | Vehicle information prompting method and device, electronic equipment, medium and vehicle |
| CN113968216A (en) * | 2020-07-25 | 2022-01-25 | 华为技术有限公司 | Vehicle collision detection method and device and computer readable storage medium |
| CN113978479A (en) * | 2021-11-26 | 2022-01-28 | 上海安亭地平线智能交通技术有限公司 | Method and device for controlling automatic driving of vehicle |
| CN113997943A (en) * | 2021-10-28 | 2022-02-01 | 山东新一代信息产业技术研究院有限公司 | Automatic driving vehicle control method, equipment and medium based on semantic clustering |
| CN114184203A (en) * | 2021-11-08 | 2022-03-15 | 重庆兰德适普信息科技有限公司 | Path planning method, electronic equipment and mobile device |
| CN114312619A (en) * | 2021-12-09 | 2022-04-12 | 华人运通(江苏)技术有限公司 | Vehicle obstacle avoidance false alarm detection method, device, medium and vehicle |
| CN114859914A (en) * | 2022-05-09 | 2022-08-05 | 广东利元亨智能装备股份有限公司 | Obstacle detection method, apparatus, device and storage medium |
| WO2022237208A1 (en) * | 2021-05-13 | 2022-11-17 | 上海仙途智能科技有限公司 | Trajectory prediction |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013127666A1 (en) * | 2012-03-01 | 2013-09-06 | Robert Bosch Gmbh | Method for automatically carrying out a driving maneuver |
| CN103754219A (en) * | 2014-02-21 | 2014-04-30 | 重庆邮电大学 | Automatic parking system of information fusion of multiple sensors |
| WO2015186647A1 (en) * | 2014-06-06 | 2015-12-10 | 日立オートモティブシステムズ株式会社 | Obstacle-information-managing device |
| CN106297395A (en) * | 2016-10-02 | 2017-01-04 | 南京邮电大学 | A kind of vehicle location being applied to parking guidance system and light belt bootstrap technique |
| CN107521493A (en) * | 2017-08-28 | 2017-12-29 | 苏州优达斯汽车科技有限公司 | A kind of automated parking system |
| CN110618420A (en) * | 2019-10-15 | 2019-12-27 | 广州小鹏汽车科技有限公司 | Ultrasonic data processing method and system, vehicle and storage medium |
-
2020
- 2020-01-22 CN CN202010072876.2A patent/CN110929702B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013127666A1 (en) * | 2012-03-01 | 2013-09-06 | Robert Bosch Gmbh | Method for automatically carrying out a driving maneuver |
| CN103754219A (en) * | 2014-02-21 | 2014-04-30 | 重庆邮电大学 | Automatic parking system of information fusion of multiple sensors |
| WO2015186647A1 (en) * | 2014-06-06 | 2015-12-10 | 日立オートモティブシステムズ株式会社 | Obstacle-information-managing device |
| CN106297395A (en) * | 2016-10-02 | 2017-01-04 | 南京邮电大学 | A kind of vehicle location being applied to parking guidance system and light belt bootstrap technique |
| CN107521493A (en) * | 2017-08-28 | 2017-12-29 | 苏州优达斯汽车科技有限公司 | A kind of automated parking system |
| CN110618420A (en) * | 2019-10-15 | 2019-12-27 | 广州小鹏汽车科技有限公司 | Ultrasonic data processing method and system, vehicle and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 吕景泉等: "《机器人技术应用》", 31 January 2012, 中国铁道出版社 * |
| 王兴松: "《Mecanum轮全方位移动机器人原理与应用》", 30 June 2018, 东南大学出版社 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111752271A (en) * | 2020-04-16 | 2020-10-09 | 北京京东乾石科技有限公司 | Trajectory prediction method, apparatus, device and storage medium |
| CN113968216B (en) * | 2020-07-25 | 2023-11-17 | 华为技术有限公司 | Vehicle collision detection method and device and computer readable storage medium |
| CN113968216A (en) * | 2020-07-25 | 2022-01-25 | 华为技术有限公司 | Vehicle collision detection method and device and computer readable storage medium |
| CN111879330A (en) * | 2020-08-05 | 2020-11-03 | 苏州挚途科技有限公司 | Method and device for planning crossing driving path, processor and automatic driving vehicle |
| TWI737437B (en) * | 2020-08-07 | 2021-08-21 | 財團法人車輛研究測試中心 | Trajectory determination method |
| CN112277930B (en) * | 2020-10-20 | 2022-06-21 | 上海欧菲智能车联科技有限公司 | Horizontal autonomous parking control method and device, vehicle and storage medium |
| CN112277930A (en) * | 2020-10-20 | 2021-01-29 | 上海欧菲智能车联科技有限公司 | Horizontal autonomous parking control method and device, vehicle and storage medium |
| WO2022237208A1 (en) * | 2021-05-13 | 2022-11-17 | 上海仙途智能科技有限公司 | Trajectory prediction |
| CN113401141B (en) * | 2021-07-12 | 2022-07-29 | 北京百度网讯科技有限公司 | Route processing method and device |
| CN113401141A (en) * | 2021-07-12 | 2021-09-17 | 北京百度网讯科技有限公司 | Route processing method and device |
| US12122369B2 (en) | 2021-07-12 | 2024-10-22 | Beijing Baidu Netcom Science Technology Co., Ltd. | Route processing method and apparatus |
| CN113734190A (en) * | 2021-09-09 | 2021-12-03 | 北京百度网讯科技有限公司 | Vehicle information prompting method and device, electronic equipment, medium and vehicle |
| CN113997943A (en) * | 2021-10-28 | 2022-02-01 | 山东新一代信息产业技术研究院有限公司 | Automatic driving vehicle control method, equipment and medium based on semantic clustering |
| CN114184203A (en) * | 2021-11-08 | 2022-03-15 | 重庆兰德适普信息科技有限公司 | Path planning method, electronic equipment and mobile device |
| CN114184203B (en) * | 2021-11-08 | 2023-12-19 | 重庆兰德适普信息科技有限公司 | Path planning method, electronic equipment and mobile device |
| CN113978479A (en) * | 2021-11-26 | 2022-01-28 | 上海安亭地平线智能交通技术有限公司 | Method and device for controlling automatic driving of vehicle |
| CN114312619A (en) * | 2021-12-09 | 2022-04-12 | 华人运通(江苏)技术有限公司 | Vehicle obstacle avoidance false alarm detection method, device, medium and vehicle |
| CN114312619B (en) * | 2021-12-09 | 2023-07-14 | 华人运通(江苏)技术有限公司 | Vehicle obstacle avoidance false alarm detection method and device, medium and vehicle |
| CN114859914A (en) * | 2022-05-09 | 2022-08-05 | 广东利元亨智能装备股份有限公司 | Obstacle detection method, apparatus, device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110929702B (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110929702B (en) | Trajectory planning method and device, electronic equipment and storage medium | |
| CN111311925B (en) | Parking space detection method and device, electronic equipment, vehicle and storage medium | |
| CN111680362B (en) | Automatic driving simulation scene acquisition method, device, equipment and storage medium | |
| JP7142739B2 (en) | GUIDANCE ROUTE IDENTIFICATION METHOD AND VEHICLE RUNNING CONTROL METHOD, DEVICE, DEVICE, STORAGE MEDIUM AND COMPUTER PROGRAM | |
| CN111731283B (en) | Automatic driving vehicle collision risk identification method and device and electronic equipment | |
| CN112572462A (en) | Automatic driving control method and device, electronic equipment and storage medium | |
| CN112526999B (en) | Speed planning method, device, electronic equipment and storage medium | |
| CN111854771A (en) | Map quality detection processing method and device, electronic equipment and storage medium | |
| CN110660219A (en) | Parking lot parking prediction method and device | |
| CN110689747B (en) | Control method and device of automatic driving vehicle and automatic driving vehicle | |
| CN111735457B (en) | Indoor navigation method and device, electronic equipment and readable storage medium | |
| CN113635912B (en) | Vehicle control method, device, equipment, storage medium and automatic driving vehicle | |
| CN111693059B (en) | Navigation method, device and equipment for roundabout and storage medium | |
| CN113844463B (en) | Vehicle control method and device based on automatic driving system and vehicle | |
| CN111368760A (en) | Obstacle detection method and device, electronic equipment and storage medium | |
| CN114179832A (en) | Lane changing method for autonomous vehicle | |
| CN114506343B (en) | Track planning method, device, equipment, storage medium and automatic driving vehicle | |
| CN110702136B (en) | Route planning method and device for vehicle, electronic equipment and readable storage medium | |
| CN111079079A (en) | Data correction method and device, electronic equipment and computer readable storage medium | |
| CN114572240A (en) | Vehicle travel control method, device, vehicle, electronic device, and storage medium | |
| CN113859264A (en) | Vehicle control method, device, electronic device and storage medium | |
| CN110866504A (en) | Method, device and equipment for acquiring marked data | |
| CN114413920A (en) | Lane data processing method, navigation method and device | |
| CN113587937A (en) | Vehicle positioning method and device, electronic equipment and storage medium | |
| CN113091737A (en) | Vehicle-road cooperative positioning method and device, automatic driving vehicle and road side 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |