CN114386721A - Path planning method, system and medium for power swapping station and power swapping station - Google Patents

Path planning method, system and medium for power swapping station and power swapping station Download PDF

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Publication number
CN114386721A
CN114386721A CN202210284865.XA CN202210284865A CN114386721A CN 114386721 A CN114386721 A CN 114386721A CN 202210284865 A CN202210284865 A CN 202210284865A CN 114386721 A CN114386721 A CN 114386721A
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vehicle
station
image
path planning
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CN114386721B (en
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王逸飞
邹积勇
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Weilai Automobile Technology Anhui Co Ltd
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Weilai Automobile Technology Anhui Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • 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
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469—Fuel consumption; Energy use; Emission aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4038—Scaling the whole image or part thereof for image mosaicing, i.e. plane images composed of plane sub-images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/32—Indexing scheme for image data processing or generation, in general involving image mosaicing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning

Abstract

The invention provides a path planning method for a power swapping station, a system, a medium and a power swapping station for executing the path planning method, wherein the path planning method comprises the following steps executed by the power swapping station: a path planning method for a power swapping station comprises the following steps executed by the power swapping station: s100: acquiring an image of a pre-set area in front of a station according to a pre-set trigger condition and determining a travelable area based on a pre-trained model and current travel environment information, wherein the current travel environment information comprises information about obstacles in the pre-set area in front of the station; s200: acquiring position information of a vehicle in a pre-set area in front of a station; s300: one or more planned paths are generated and transmitted to the vehicle based on the travelable region and the position information of the vehicle. By dividing the image into different partial images and determining the drivable partial regions in the partial images with different accuracies, the amount of computation can be further reduced while ensuring the computation accuracy.

Description

Path planning method, system and medium for power swapping station and power swapping station
Technical Field
The present invention relates to a path planning method for a power swapping station, a system, a medium and a power swapping station for performing such a path planning method.
Background
At present, two modes of charging and replacing the whole vehicle (namely battery replacement) mainly exist for energy supply of the electric vehicle. The whole vehicle charging mode can be divided into alternating current slow charging and direct current fast charging, wherein the alternating current slow charging requires long time and is limited by a parking lot. In addition, although the direct current quick charging has large power and short charging time, the direct current quick charging has large impact on a power grid and can also reduce the service life of a battery. On the contrary, the battery replacement mode can reduce the damage to the service life of the battery while realizing the rapid energy supply to the electric automobile. In addition, the battery replacement mode can realize peak shaving energy storage of power loads of a power grid and improve comprehensive utilization efficiency of power equipment.
The driving environment in the area in front of the power exchange station is relatively complex, especially when the power exchange station is arranged at an expressway, so that necessary assistance is necessary for the parking process of the vehicle into the power exchange station and the exit process of the vehicle from the power exchange station. For example, a planned path is provided for the vehicle during parking in order to perform an automatic parking operation or to assist the driver in performing a parking operation. In addition, in the case of a box-type battery replacement station, it is difficult for a driver to control the total drivable area in front of the station due to the line of sight being blocked during the driving-out process, which may cause the vehicle to be scratched or collided.
Disclosure of Invention
According to different aspects, the invention is directed to a path planning method for a power swapping station, a system, a medium and a power swapping station for performing such a path planning method.
Furthermore, the present invention is also directed to solve or alleviate other technical problems of the prior art.
The present invention solves the above problems by providing a path planning method for a power swapping station, and specifically, the path planning method includes the following steps performed by the power swapping station:
s100: acquiring an image of a pre-set area in front of a station according to a preset trigger condition and determining a travelable area based on a pre-trained model and current travel environment information, wherein the current travel environment information comprises information about obstacles in the pre-set area in front of the station;
s200: acquiring position information of a vehicle in a preset area in front of the station;
s300: generating one or more planned paths based on the travelable region and the position information of the vehicle and transmitting the one or more planned paths to the vehicle;
wherein, step S100 includes the following substeps:
s110: dividing an image to be input into the model into a far-end sub-image and a near-end sub-image based on the distance between the obstacle and a camera for acquiring the image;
s120: respectively acquiring drivable sub-areas in the far-end sub-image and the near-end sub-image according to different accuracies on the basis of the model;
s130: and splicing the travelable sub-regions to obtain an integral travelable region.
According to the path planning method provided by one aspect of the present invention, in step S120, the drivable sub-regions in the far-end sub-image and the near-end sub-image are acquired based on motion information of a vehicle, where the motion information includes a motion direction and/or a motion speed of the vehicle.
According to the path planning method proposed by an aspect of the present invention, step S100 further includes the following sub-steps:
s140: in response to detecting a non-stationary obstacle, obtaining a motion parameter of the non-stationary obstacle and predicting an occupancy channel of the non-stationary obstacle based on the motion parameter;
s150: and correcting the whole travelable area based on the occupied passage.
According to the path planning method provided by one aspect of the invention, in step S200, position information of a vehicle in a preset area in front of the station is acquired based on the identifier of the power swapping station.
According to the path planning method proposed by one aspect of the present invention, step S200 includes the following sub-steps:
s210: acquiring an image of a pre-set area in front of a station;
s220: and acquiring the position information of the vehicle in the preset area in front of the station according to an image comparison algorithm based on the position of the marker in the image.
According to the path planning method provided by one aspect of the invention, the marker comprises a planar marker and a stereoscopic marker which are arranged in a preset area in front of the station.
According to the path planning method provided by one aspect of the invention, the preset trigger condition comprises a passive trigger condition triggered by a user and/or an active trigger condition triggered by vehicle behaviors.
The path planning method provided by one aspect of the invention further comprises the following steps:
s400: attribute information of a vehicle is acquired and it is determined whether the vehicle is a service vehicle based on the attribute information.
The path planning method provided by one aspect of the invention further comprises the following steps:
s500: in response to receiving a confirmation command characterizing a theoretically planned path selected by a vehicle, transmitting parameters of the theoretically planned path to the vehicle.
According to another aspect of the present invention, there is also provided a system, which is disposed in a power swapping station and can be used for path planning in the power swapping station, including:
a memory;
a processor;
a computer program stored on the memory and executable on the processor, the execution of the computer program causing the following steps to be performed:
s100: acquiring an image of a pre-set area in front of a station according to a preset trigger condition and determining a travelable area based on a pre-trained model and current travel environment information, wherein the current travel environment information comprises information about obstacles in the pre-set area in front of the station;
s200: acquiring position information of a vehicle in a preset area in front of the station;
s300: generating one or more planned paths based on the travelable region and the position information of the vehicle and transmitting the one or more planned paths to the vehicle;
wherein, step S100 includes the following substeps:
s110: dividing an image to be input into the model into a far-end sub-image and a near-end sub-image based on the distance between the obstacle and a camera for acquiring the image;
s120: respectively acquiring drivable sub-areas in the far-end sub-image and the near-end sub-image according to different accuracies on the basis of the model;
s130: and splicing the travelable sub-regions to obtain an integral travelable region.
According to another aspect of the present invention, in step S120, the drivable regions in the far-end sub-image and the near-end sub-image are obtained based on motion information of a vehicle, where the motion information includes a motion direction and a motion speed of the vehicle.
According to another aspect of the present invention, the step S100 further includes the following sub-steps:
s140: in response to detecting a non-stationary obstacle, obtaining a motion parameter of the non-stationary obstacle and predicting an occupancy channel of the non-stationary obstacle based on the motion parameter;
s150: and correcting the whole travelable area based on the occupied passage.
According to another aspect of the present invention, in step S200, position information of a vehicle in a preset area in front of the station is obtained based on the identifier of the power swapping station.
According to another aspect of the present invention, the step S200 includes the following sub-steps:
s210: acquiring an image of a pre-set area in front of a station;
s220: and acquiring the position information of the vehicle in the preset area in front of the station according to an image comparison algorithm based on the position of the marker in the image.
According to another aspect of the present invention, a system is provided wherein the marker comprises a planar marker and a volumetric marker disposed in a pre-defined area in front of the station.
According to another aspect of the present invention, a system is proposed, wherein the preset trigger condition comprises a passive trigger condition triggered by a user and/or an active trigger condition triggered by a vehicle behavior.
According to a further aspect of the invention, a system is proposed, the execution of the computer program further causing the following steps to be performed:
s400: attribute information of a vehicle is acquired and it is determined whether the vehicle is a service vehicle based on the attribute information.
According to a further aspect of the invention, a system is proposed, the execution of the computer program further causing the following steps to be performed:
s500: in response to receiving a confirmation command characterizing a theoretically planned path selected by a vehicle, transmitting parameters of the theoretically planned path to the vehicle.
According to a further aspect of the present invention, there is also provided a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the path planning method for a power swapping station set forth above.
According to another aspect of the present invention, there is also provided a power swapping station, which includes the system for the path planning method for the power swapping station set forth above.
By dividing the image into different partial images and determining the drivable partial regions in the partial images with different accuracies, the amount of computation can be further reduced while ensuring the computation accuracy.
Drawings
The above and other features of the present invention will become apparent with reference to the accompanying drawings, in which,
fig. 1 to 5 show the main steps of a path planning method according to an embodiment of the invention;
FIG. 6 shows a schematic representation of an overall travelable region determined by the method according to the invention;
FIG. 7 shows a schematic diagram of a process for acquiring a drivable subregion;
fig. 8 shows a schematic diagram of a system for path planning in a swap station according to an embodiment of the invention.
Detailed Description
It is easily understood that according to the technical solution of the present invention, a person skilled in the art can propose various alternative structures and implementation ways without changing the spirit of the present invention. Therefore, the following detailed description and the accompanying drawings are merely illustrative of the technical aspects of the present invention, and should not be construed as all of the present invention or as limitations or limitations on the technical aspects of the present invention.
The terms of orientation of up, down, left, right, front, back, top, bottom, and the like referred to or may be referred to in this specification are defined relative to the configuration shown in the drawings, and are relative terms, and thus may be changed correspondingly according to the position and the use state of the device. Therefore, these and other directional terms should not be construed as limiting terms. Furthermore, the terms "first," "second," "third," and the like are used for descriptive and descriptive purposes only and not for purposes of indication or implication as to the relative importance of the respective components.
The invention relates to a method for path planning in a power swapping station, comprising the following steps, with reference to fig. 1 to 5, which are performed by the power swapping station:
s100: acquiring an image of a pre-set area in front of a station according to a preset trigger condition and determining a travelable area based on a pre-trained model and current travel environment information, wherein the current travel environment information comprises information about obstacles in the pre-set area in front of the station;
s200: acquiring position information of a vehicle in a preset area in front of the station;
s300: generating one or more planned paths based on the travelable region and the position information of the vehicle and transmitting the one or more planned paths to the vehicle;
wherein, step S100 includes the following substeps:
s110: dividing an image to be input into the model into a far-end sub-image and a near-end sub-image based on the distance between the obstacle and a camera for acquiring the image;
s120: respectively acquiring drivable sub-areas in the far-end sub-image and the near-end sub-image according to different accuracies on the basis of the model;
s130: and splicing the travelable sub-regions to obtain an integral travelable region.
It should be noted that the above-mentioned (and the following-mentioned) step names are only used for distinguishing between steps and for facilitating the reference of the steps, and do not represent the sequential relationship between the steps, and the flow charts including the figures are only examples for performing the method. Steps may be performed in various orders or simultaneously without significant conflict.
It should be noted that the replacement station mentioned here can be understood as a building-type replacement station that is separated from the outside world, on the one hand, and as a replacement device for replacing a vehicle power battery, for example, a stationary, movable or foldable replacement device, on the other hand. This type of charging station can also be referred to as a charging station.
It should be noted that the pre-station preset area is located in front of the power conversion station and is used for providing a necessary parking area and a necessary driving area. Accordingly, the "driving-before-station area" can be understood as an area occupied by the whole pre-set area before the station except the obstacle, that is, an area where the vehicle can drive. The pre-station travelable area is identical to the pre-station preset area without obstacles. Further, the concept of "obstacle" can relate to pedestrians, living beings, obstacle avoidance, non-service vehicles, etc., which are divided into stationary obstacles or non-stationary obstacles.
In this case, one or more planned routes are provided for the service vehicle of the power conversion station based on the obtained drivable area (which may also be referred to as a pre-station drivable area), so that the driver can be assisted in completing parking or exiting operations or in completing automatic parking or exiting operations. The travelable area can be represented here in an abstract topological map, see fig. 6. In place of the vehicle itself, the autonomous planning of the route by the station end (power exchange station) can avoid parking errors or driving errors due to vehicle sensor failures (e.g., a camera is contaminated or blocked) to some extent. In addition, in the case of point-to-point path planning (specifically, for example, path planning from a predetermined parking point to a fixed line on a power conversion platform), the power conversion station can recall the stored planned path and perform path planning based on the retrieved planned path, which is advantageous in memorizing the rules of parking and reduces the data processing amount to some extent.
Considering the battery storage capacity of the battery replacement station, before performing the path planning, it should be determined whether the vehicle is a vehicle to be serviced, so as to avoid the phenomenon of queue insertion and reduce the user experience. Specifically, the method according to the invention further comprises a step S400 (authentication step): attribute information of a vehicle is acquired and it is determined whether the vehicle is a service vehicle based on the attribute information. The attribute information of the vehicle can include a model, a license plate number, owner information, etc. of the vehicle. This step can be implemented by means of the owner's cell phone or other wireless communication device.
The generated topological map can be transmitted from the power switching station to the cloud and can be shared with other control processes, which is advantageous, for example, if a plurality of vehicles to be serviced or serviced vehicles are simultaneously traveling in a pre-set area in front of the station. In addition, the station side can communicate with the vehicle side (vehicle) through the cloud platform or other relay devices and share the generated multiple planned paths to the vehicle, so that the vehicle side or a driver can select and confirm the required planned paths, the control right of the vehicle side on the vehicle is guaranteed to a certain extent, and the user experience is improved accordingly. The plurality of planned paths can be designed as short routes, as easy to operate planned paths, as well as safer planned paths. The attributes of the plurality of planned paths can be provided, for example, by the swap station or by the controller of the vehicle volume itself. The provision of a plurality of planned paths is particularly advantageous in the case of assisted driving, in view of the non-uniformity of the driving level of the driver.
Accordingly, the path planning method according to the present invention can optionally further include step S500: in response to receiving a confirmation command characterizing a theoretically planned path selected by the vehicle, parameters of the theoretically planned path are transmitted to the vehicle, which parameters can include steering wheel torque and/or vehicle speed.
Alternatively, the preset trigger condition in step S100 can relate to a passive trigger condition triggered by the user. For example, after receiving a path planning request from a user, the power swapping station starts to perform the above steps according to the present invention.
Optionally, the preset trigger condition can also relate to an active trigger condition triggered by the vehicle behavior. For example, when the parking path planning is performed, the above steps according to the present invention are started when it is detected that the vehicle is parked to the preset parking space. The parking space can be configured in a simple manner as a rectangular area in front of, in particular directly in front of, the switching station (for example, a rectangular area with a length of 5 meters and a width of 3 meters).
Furthermore, for the acquisition of the images input into the model, it can be achieved by a single camera with a large coverage. Alternatively, the image acquisition process can also be carried out by two or more cameras arranged at the power station in order to cover the pre-set area in front of the power station more completely and thus to increase the redundancy and reliability of the determination. In the case where a plurality of cameras are provided, specific positions and shooting angles of the individual cameras in the power station coordinate system are determined in advance, whereby images shot by the respective cameras can be stitched together and thus an image of the entire pre-set area in front of the power station can be obtained. It should be noted that this image stitching process can be omitted in the case where only a single camera is provided.
Step S100 will now be explained in more detail with reference to fig. 7. It should be mentioned at the outset that the image that is or is to be input into the model can be, but is not limited to, divided into two partial images, the number of which can be selected depending on the computing power of the computing device. "distal" and "proximal" refer to a camera or infrared camera used to capture the image, where "distal" refers to an area away from the camera, i.e., an area of the image away from the camera, and "proximal" refers to an area close to the camera, i.e., an area of the image close to the camera. For an image, the cell size of the near-end sub-image close to the camera represents a smaller length than the far-end sub-image far from the camera, e.g. a cell size of one centimeter in the far-end sub-image can represent a distance of ten centimeters or more, while the cell size of one centimeter in the near-end sub-image represents a distance of only 0.5 centimeters. Therefore, if the travelable region is obtained with the same accuracy (which can also be referred to as granularity), this causes a certain error. By dividing the image into different sub-images and acquiring travelable sub-regions in the respective sub-images with different accuracies, the computational accuracy can be improved to a certain extent and the computational effort can be reduced, mainly because precise control can be performed according to specific needs without retraining the model.
Referring to fig. 7, (where the upper left part is the far-end sub-image and the lower left part is the near-end sub-image and the right part is the entire travelable region after stitching, the obstacles are schematically represented by hatched triangles and rectangles for clarity, and the travelable regions are respectively represented by the bar regions of the dashed box columns), in order to obtain a higher computational accuracy, the far-end sub-image is image-processed with a higher accuracy than the near-end sub-image, that is, the travelable region in the far-end sub-image is smaller in size. By determining whether the boundary of the travelable sub-region is the ground, obtaining the final boundary of the travelable sub-region and thereby generating the boundary of the entire pre-station travelable region, this method can be referred to as a guillotine-type determination algorithm, which can be, but is not limited to, classification, regression, etc. methods.
Alternatively, the model can be constructed, for example, as a convolutional neural network model, which is trained in advance by a plurality of sets of training data including the sample images and the corresponding labeling information. The convolutional neural network model comprises a convolutional layer, a normalized layer, an activation layer, a maximum pooling layer, a full-link layer and an output layer, which are not described in detail. In order to diversify the training data set and increase the robustness of the model during the training of the model, the training data set can optionally also be augmented in an online data-enhancing manner.
Optionally, when determining the drivable partial regions of the individual partial images, the parts of the partial images can be deleted in a targeted manner on the basis of movement information of the vehicle, which movement information comprises the direction of movement and the speed of movement of the vehicle, or of a driving task of the vehicle, which driving task comprises driving into or out of the battery exchange station. Specifically, when the travel task of the service vehicle is acquired in advance, detection of an area that the service vehicle does not relate to can be omitted according to the travel task. As shown in fig. 6 (in which the vehicle enclosed in dashed lines is the service vehicle and is in the process of parking into the power station, and accordingly the vehicle above the left can be regarded as an obstacle, and the travelable sub-areas are shown equally sized for clarity), the detection of the area behind the service vehicle and remote from the power station entrance is omitted. In this way, the calculation processing amount can be further reduced.
In a further embodiment, the image processing of the rear area in the direction away from the direction of movement of the vehicle can also be omitted in view of the actual driving situation, i.e. the image processing is carried out in compliance with rules that do not lead over large obstacles, for example vehicles.
Optionally, the path planning method according to the invention can also comprise a travelable region correction step, taking into account the possible movement tendency of the obstacle, i.e. the method comprises the following sub-steps:
s140: in response to detecting a non-stationary obstacle, obtaining a motion parameter of the non-stationary obstacle and predicting an occupancy channel of the non-stationary obstacle based on the motion parameter;
s150: and correcting the whole travelable area based on the occupied passage.
In this case, the travel-capable region finally determined is the region in which the occupied lane is removed. In addition, considering that the vehicle speed is low during parking, the occupied lane of the obstacle that leaves quickly can be regarded as the in-front travelable area.
The vehicle position acquisition step (step S200) according to the method can be realized by means of an identifier of the power exchange station. For example, the actual position of the vehicle is acquired in the charging station coordinate system (that is, the image coordinate system) directly or based on the marker based on the image of the pre-station preset area acquired by the camera device of the charging station. In addition, this step can also be implemented on the basis of an image acquired by a vehicle camera, and specifically, the vehicle position acquisition step includes the following substeps:
s210: acquiring an image of a preset area in front of a station,
s220: and acquiring the position information of the vehicle in the preset area in front of the station according to an image comparison algorithm based on the position of the marker in the image.
Here, the marker can relate to a planar marker (e.g., an arrow-shaped marker, a single rectangular-shaped marker, a triangular marker, a polygonal marker, or a two-dimensional code marker) or a three-dimensional marker arranged in a pre-set area in front of the station. Furthermore, the identifier can also relate to a component of the charging station itself, for example a ramp at the entrance of the charging station. The position of the marker can be selected as required, and the marker can be arranged at the entrance of the power station or at any other position.
Referring to fig. 8, a schematic diagram of a proposed system 100 according to another aspect of the present invention is shown, which includes a memory 110 (e.g., a non-volatile memory such as a flash memory, a ROM, a hard disk drive, a magnetic disk, an optical disk), a processor 120, and a computer program 130 stored on the memory 110 and executable on the processor 120, the execution of the computer program implementing a method for path planning in a swap station according to one or more embodiments of the present invention. For the description of the system, reference may be made to the above description of the method for path planning in the swapping station, which is not described again.
Optionally, the system 100 can be a cloud computing device. Illustratively, the memory 110 and the processor 120 as cloud computing resources can be located not only within the same physical device (e.g., the same server), but also at different physical devices (e.g., different servers).
Optionally, the system 100 can also be an edge computing device, which is arranged in the swapping station. In this way, the vehicle is routed close to the data source (i.e., in the local edge computing layer), thereby ensuring real-time and stability of data processing. In addition, with the aid of one or more edge computing devices, the computing load of the cloud can be reduced to some extent and the implementation of the internet of things is facilitated.
It should be noted that, for the description of the system according to the present invention, reference can be made to the explanation of the path planning method in the swapping station according to the present invention, and details thereof are not repeated.
In summary, by acquiring the station-ahead drivable area and providing one or more planned paths for the service vehicle of the power swapping station based on the acquired area, the driver can be assisted in completing parking or exiting operations or completing automatic parking or exiting operations. In an embodiment of the present invention, by dividing the image into different sub-images and identifying the drivable sub-regions with different accuracies, the amount of computation can be further reduced and the computational flexibility can be improved while ensuring the computational accuracy. In one embodiment of the invention, the driver can be ensured to control the vehicle to some extent by selecting the required planned path by the vehicle end or the user. In another embodiment of the invention, the path planning of the vehicle is performed by means of the edge computing device, so that the real-time performance and stability of data transmission and processing can be ensured, and the computing load of the cloud can be reduced to a certain extent.
The invention further relates to a computer-readable storage medium for carrying out a method for path planning for a vehicle in a battery charging station according to one or more embodiments of the invention. Computer-readable storage media, as referred to herein, include various types of computer storage media, and may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, computer-readable storage media may include RAM, ROM, EPROM, E2PROM, registers, hard disk, a removable disk, a CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other transitory or non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. The description of the computer-readable storage medium according to the invention can refer to the explanations for the method according to the invention, which are not repeated here.
Finally, the invention also proposes a swapping station comprising a system according to one or more embodiments of the invention. For the description of the power swapping station according to the present invention, reference can be made to the explanations of the method according to the present invention, which are not described in detail here.
It should be understood that all of the above preferred embodiments are exemplary and not restrictive, and that various modifications and changes in the specific embodiments described above, which would occur to persons skilled in the art upon consideration of the above teachings, are intended to be within the scope of the invention.

Claims (20)

1. A path planning method for a power swapping station is characterized by comprising the following steps executed by the power swapping station:
s100: acquiring an image of a pre-set area in front of a station according to a preset trigger condition and determining a travelable area based on a pre-trained model and current travel environment information, wherein the current travel environment information comprises information about obstacles in the pre-set area in front of the station;
s200: acquiring position information of a vehicle in a preset area in front of the station;
s300: generating one or more planned paths based on the travelable region and the position information of the vehicle and transmitting the one or more planned paths to the vehicle;
wherein, step S100 includes the following substeps:
s110: dividing an image to be input into the model into a far-end sub-image and a near-end sub-image based on the distance between the obstacle and a camera for acquiring the image;
s120: respectively acquiring drivable sub-areas in the far-end sub-image and the near-end sub-image according to different accuracies on the basis of the model;
s130: and splicing the travelable sub-regions to obtain an integral travelable region.
2. The path planning method according to claim 1, wherein in step S120, drivable sub-regions in the far-end sub-image and the near-end sub-image are obtained based on motion information of a vehicle, wherein the motion information includes a motion direction and/or a motion speed of the vehicle.
3. The path planning method according to claim 2, wherein step S100 further comprises the following sub-steps:
s140: in response to detecting a non-stationary obstacle, obtaining a motion parameter of the non-stationary obstacle and predicting an occupancy channel of the non-stationary obstacle based on the motion parameter;
s150: and correcting the whole travelable area based on the occupied passage.
4. The path planning method according to claim 1, wherein in step S200, position information of a vehicle in a preset area in front of the station is acquired based on the identifier of the power swapping station.
5. The path planning method according to claim 4, wherein step S200 comprises the following sub-steps:
s210: acquiring an image of a pre-set area in front of a station;
s220: and acquiring the position information of the vehicle in the preset area in front of the station according to an image comparison algorithm based on the position of the marker in the image.
6. The path planning method according to claim 5, wherein the markers include a planar marker and a stereoscopic marker disposed in a pre-set area in front of the station.
7. The path planning method according to any one of claims 1 to 6, wherein the preset trigger conditions comprise passive trigger conditions triggered by a user and/or active trigger conditions triggered by vehicle behavior.
8. The path planning method according to any one of claims 1 to 6, characterized in that the path planning method further comprises the steps of:
s400: attribute information of a vehicle is acquired and it is determined whether the vehicle is a service vehicle based on the attribute information.
9. The path planning method according to any one of claims 1 to 6, characterized in that the path planning method further comprises the steps of:
s500: in response to receiving a confirmation command characterizing a theoretically planned path selected by a vehicle, transmitting parameters of the theoretically planned path to the vehicle.
10. A system disposed in a power swapping station and operable to perform path planning in the power swapping station, comprising:
a memory;
a processor;
a computer program stored on the memory and executable on the processor, the execution of the computer program causing the following steps to be performed:
s100: acquiring an image of a pre-set area in front of a station according to a preset trigger condition and determining a travelable area based on a pre-trained model and current travel environment information, wherein the current travel environment information comprises information about obstacles in the pre-set area in front of the station;
s200: acquiring position information of a vehicle in a preset area in front of the station;
s300: generating one or more planned paths based on the travelable region and the position information of the vehicle and transmitting the one or more planned paths to the vehicle;
wherein, step S100 includes the following substeps:
s110: dividing an image to be input into the model into a far-end sub-image and a near-end sub-image based on the distance between the obstacle and a camera for acquiring the image;
s120: respectively acquiring drivable sub-areas in the far-end sub-image and the near-end sub-image according to different accuracies on the basis of the model;
s130: and splicing the travelable sub-regions to obtain an integral travelable region.
11. The system according to claim 10, wherein in step S120, drivable sub-regions in the far-end sub-image and the near-end sub-image are acquired based on motion information of a vehicle, wherein the motion information comprises a motion direction and a motion speed of the vehicle.
12. The system according to claim 11, wherein step S100 further comprises the sub-steps of:
s140: in response to detecting a non-stationary obstacle, obtaining a motion parameter of the non-stationary obstacle and predicting an occupancy channel of the non-stationary obstacle based on the motion parameter;
s150: and correcting the whole travelable area based on the occupied passage.
13. The system according to claim 10, wherein in step S200, position information of a vehicle in a preset area in front of the station is acquired based on the identifier of the power swapping station.
14. The system according to claim 13, wherein step S200 comprises the sub-steps of:
s210: acquiring an image of a pre-set area in front of a station;
s220: and acquiring the position information of the vehicle in the preset area in front of the station according to an image comparison algorithm based on the position of the marker in the image.
15. The system of claim 14, wherein the markers comprise a planar marker and a volumetric marker disposed in a pre-set area in front of the station.
16. The system according to any one of claims 10 to 15, characterized in that the preset trigger conditions comprise passive trigger conditions triggered by a user and/or active trigger conditions triggered by vehicle behavior.
17. A system according to any one of claims 10 to 15, wherein execution of the computer program further causes the following steps to be performed:
s400: attribute information of a vehicle is acquired and it is determined whether the vehicle is a service vehicle based on the attribute information.
18. A system according to any one of claims 10 to 15, wherein execution of the computer program further causes the following steps to be performed:
s500: in response to receiving a confirmation command characterizing a theoretically planned path selected by a vehicle, transmitting parameters of the theoretically planned path to the vehicle.
19. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a path planning method for a swap station according to any one of claims 1 to 9.
20. A power swapping station comprising a system according to any of claims 10 to 18.
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Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102508489A (en) * 2011-09-28 2012-06-20 山东电力集团公司临沂供电公司 Vehicle guide system of electric vehicle charging station and guide method
WO2013050194A1 (en) * 2011-10-07 2013-04-11 Robert Bosch Gmbh Method for navigating a vehicle to a parking place
CN106485233A (en) * 2016-10-21 2017-03-08 深圳地平线机器人科技有限公司 Drivable region detection method, device and electronic equipment
CN111319615A (en) * 2020-03-16 2020-06-23 湖北亿咖通科技有限公司 Intelligent passenger-replacing parking method, computer-readable storage medium and electronic device
CN111386530A (en) * 2018-12-29 2020-07-07 深圳市大疆创新科技有限公司 Vehicle detection method and apparatus
CN111415525A (en) * 2020-03-22 2020-07-14 华南理工大学 Unmanned parking system based on automatic passenger-replacing parking technology
CN111723960A (en) * 2019-03-20 2020-09-29 浙江吉智新能源汽车科技有限公司 Electric automobile battery replacement planning method and system
EP3750742A1 (en) * 2019-06-13 2020-12-16 Volkswagen Ag Parking system for vehicles
CN112419776A (en) * 2020-10-16 2021-02-26 爱驰汽车（上海）有限公司 Autonomous parking method and device, automobile and computing equipment
CN112562408A (en) * 2020-11-30 2021-03-26 北京停简单信息技术有限公司 Global path planning method, device and system based on vehicle-road cooperation
KR102274234B1 (en) * 2020-10-15 2021-07-07 주식회사 디시원이엔지 One-stop automatic parking guidance system
CN113506456A (en) * 2021-06-01 2021-10-15 上海追势科技有限公司 Autonomous parking navigation method based on parking lot map data guidance
CN113665591A (en) * 2021-09-28 2021-11-19 上海焱眼鑫睛智能科技有限公司 Unmanned control method, device, equipment and medium

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102508489A (en) * 2011-09-28 2012-06-20 山东电力集团公司临沂供电公司 Vehicle guide system of electric vehicle charging station and guide method
WO2013050194A1 (en) * 2011-10-07 2013-04-11 Robert Bosch Gmbh Method for navigating a vehicle to a parking place
CN106485233A (en) * 2016-10-21 2017-03-08 深圳地平线机器人科技有限公司 Drivable region detection method, device and electronic equipment
CN111386530A (en) * 2018-12-29 2020-07-07 深圳市大疆创新科技有限公司 Vehicle detection method and apparatus
CN111723960A (en) * 2019-03-20 2020-09-29 浙江吉智新能源汽车科技有限公司 Electric automobile battery replacement planning method and system
EP3750742A1 (en) * 2019-06-13 2020-12-16 Volkswagen Ag Parking system for vehicles
CN111319615A (en) * 2020-03-16 2020-06-23 湖北亿咖通科技有限公司 Intelligent passenger-replacing parking method, computer-readable storage medium and electronic device
CN111415525A (en) * 2020-03-22 2020-07-14 华南理工大学 Unmanned parking system based on automatic passenger-replacing parking technology
KR102274234B1 (en) * 2020-10-15 2021-07-07 주식회사 디시원이엔지 One-stop automatic parking guidance system
CN112419776A (en) * 2020-10-16 2021-02-26 爱驰汽车（上海）有限公司 Autonomous parking method and device, automobile and computing equipment
CN112562408A (en) * 2020-11-30 2021-03-26 北京停简单信息技术有限公司 Global path planning method, device and system based on vehicle-road cooperation
CN113506456A (en) * 2021-06-01 2021-10-15 上海追势科技有限公司 Autonomous parking navigation method based on parking lot map data guidance
CN113665591A (en) * 2021-09-28 2021-11-19 上海焱眼鑫睛智能科技有限公司 Unmanned control method, device, equipment and medium

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