CN116238482A - Parking path planning method, device, equipment and storage medium - Google Patents
Parking path planning method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a parking path planning method, a device, equipment and a storage medium, which comprise the following steps: determining a candidate parking path according to the vehicle position information in response to the automatic parking instruction; determining candidate parking information corresponding to the candidate parking path, and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space. The target parking path capable of enabling the user to have the best parking experience can be automatically planned according to the candidate parking information, and therefore the automatic parking experience of the user is improved.
Description
Technical Field
The embodiment of the invention relates to the field of computers, in particular to a parking path planning method, a device, equipment and a storage medium.
Background
At present, more and more vehicles are equipped with an automatic parking function, and the currently adopted automatic parking method is to determine a parking space based on the position condition of the vehicles, and then to perform path planning according to a path planning algorithm according to the vehicle position and the parking space position of the parking space so as to determine an automatic parking path. On the basis, the parking experience of a driver is not considered, the problems that the steering wheel rotates too fast or the gear shifting times are too many in the process of parking the vehicle in a parking space can exist, the automatic parking experience of the driver is affected, the driver can possibly generate panic emotion, the using times of automatic parking are reduced, and meanwhile, the parking position of the vehicle can not meet the requirements of the driver to a certain extent after the parking is completed. Therefore, how to improve the intelligent degree of the automatic parking function of the vehicle, improve the automatic parking experience of a driver, and meet the requirement of the driver on the parking comfort level is a problem to be solved.
Disclosure of Invention
The invention provides a parking path planning method, a device, equipment and a storage medium, which can improve the intelligent level of automatic parking and improve the automatic parking experience of users.
According to an aspect of the present invention, there is provided a parking path planning method including:
determining a candidate parking path according to the vehicle position information in response to the automatic parking instruction;
determining candidate parking information corresponding to the candidate parking path, and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space.
According to another aspect of the present invention, there is provided a parking path planning apparatus including:
the candidate parking path determining module is used for determining a candidate parking path according to the vehicle position information in response to the automatic parking instruction;
the target parking path determining module is used for determining candidate parking information corresponding to the candidate parking path and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of parking path planning according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the parking path planning method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, a candidate parking path is determined according to vehicle position information in response to an automatic parking instruction; determining candidate parking information corresponding to the candidate parking path, and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space. According to the scheme, the problem that the steering wheel rotates too fast or the gear shifting times of the vehicle are too many to influence the parking experience of a driver in the automatic parking process when the vehicle automatically parks is solved. The method and the device have the advantages that when the vehicle automatically parks, the influence of steering wheel rudder rubbing times, parking gear shifting times and parking space attributes on the parking experience of the user in the vehicle parking process is fully considered, and a target parking path which can enable the user to have the best parking experience is automatically planned according to the candidate parking information through a parking path planning model, so that the automatic parking experience of the user is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a parking path planning method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a parking path planning method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a parking path planning apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "candidate" and "target" and the like in the description of the present invention and the claims and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "includes," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a parking path planning method according to an embodiment of the present invention, where the embodiment is applicable to a situation where a parking path is planned for voice instructions, and is particularly applicable to a situation where a parking path planning model is constructed, so that target instruction information is determined according to voice data to be recognized and face data to be recognized through the parking path planning model. The method may be performed by a parking path planning device, which may be implemented in hardware and/or software, which may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, responding to the automatic parking instruction, and determining a candidate parking path according to the vehicle position information.
The candidate parking path is a parking path determined according to the position information of the vehicle through a vehicle path planning algorithm.
Specifically, when an automatic parking instruction is acquired, vehicle position information is acquired according to the vehicle positioning device, and parking space position information of the candidate parking space is determined according to the vehicle position information, the destination position and the map data. The candidate parking space may be a parking space having a distance from the destination location less than a space distance threshold. The parking space distance threshold value can be set according to actual needs. And determining a candidate parking path corresponding to each candidate parking space according to the vehicle position information and the parking space position information of each candidate parking space through a vehicle path planning algorithm.
S120, candidate parking information corresponding to the candidate parking paths is determined, and a target parking path is determined according to a pre-trained parking path planning model and the candidate parking information.
The candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space.
It should be noted that, when the vehicle performs automatic parking, the steering wheel rotation speed of the vehicle is too fast in the automatic parking process, or the gear shifting times of the vehicle are too many, which can affect the driving experience of the driver. Therefore, when planning a parking path, the relationship between the parking distance and the steering number of the steering wheel, the parking shift number, the parking starting position and the parking destination of the vehicle in the parking process needs to be balanced, and the parking path with optimal user experience is selected.
The parking path planning model is a neural network model for determining a target parking path from candidate parking paths according to candidate parking information of the candidate parking paths of the vehicle.
Specifically, the candidate parking information is used as model input data of a pre-trained parking path planning model, and candidate path evaluation information of each candidate parking path is determined according to the output data of the parking path planning model. And determining candidate path evaluation information with highest evaluation index as target path evaluation information, and taking a candidate parking path corresponding to the target path evaluation information as a target parking path.
For example, the method for determining the candidate parking information corresponding to the parking path may be: determining candidate parking road conditions of the candidate parking paths; according to the candidate parking road conditions of the candidate parking paths, the number of candidate parking steering wheel rudder rubbing times and the number of candidate parking gear shifting times when the vehicle parks based on the candidate parking paths are determined; and taking the candidate parking steering wheel rudder rubbing times, the candidate parking gear shifting times, the candidate parking road conditions and the candidate parking space attributes of the candidate parking spaces as candidate parking information of the candidate parking paths.
The candidate parking road conditions of the candidate parking path include, but are not limited to, obstacle position information on the candidate parking path, a parking path length, and a parking path corner number. The parking space attributes of the candidate parking spaces can be horizontal spaces, vertical spaces or trapezoidal spaces.
It will be appreciated that the obstacle location information, the parking path length, and the number of parking path turns on the candidate parking path may affect the number of candidate parking steering wheel rubs and the number of candidate parking shifts while the vehicle is parking on the candidate parking path. When a vehicle is driven into a parking space with different parking space attributes, the required steering wheel steering times and the parking gear shifting times are different, so that the candidate parking space attributes of the candidate parking steering wheel steering times, the candidate parking gear shifting times, the candidate parking road conditions and the candidate parking space need to be comprehensively considered when the candidate parking path is evaluated.
Specifically, the position information of the obstacle on the candidate parking path and the length of the parking path are used as the candidate parking road conditions of the candidate parking path. And determining the number of candidate parking steering wheel rubdown and the number of candidate parking gear shifts when the vehicle parks based on the candidate parking path according to the candidate parking road conditions of the candidate parking path and the candidate parking space attribute of the candidate parking space. And taking the candidate parking steering wheel rudder rubbing times, the candidate parking gear shifting times, the candidate parking road conditions and the candidate parking space attributes of the candidate parking spaces as candidate parking information of the candidate parking paths.
It can be appreciated that the above scheme perfects the candidate parking information, so that when the target parking path is determined according to the candidate parking information, factors of the image target parking path are comprehensively considered, and the accuracy of the target parking path is improved.
For example, the target parking path may be determined by the following substeps:
s1201, a pre-trained parking path planning model is adopted, and candidate path evaluation information of the candidate parking paths is determined according to the candidate parking information.
Specifically, the candidate parking information is used as model input data of a pre-trained parking path planning model, and candidate path evaluation information of the candidate parking path is determined through model output data of the parking path planning model.
S1202, determining target path evaluation information from the candidate path evaluation information through a parking path planning model, and taking a candidate parking path corresponding to the target path evaluation information as a target parking path.
And determining candidate path evaluation information with the highest evaluation index as target path evaluation information through a parking path planning model, and taking a candidate parking path corresponding to the target path evaluation information as a target parking path.
According to the scheme, the target parking path is determined from the candidate parking paths according to the candidate path evaluation information of the candidate parking paths, and the parking path with higher user evaluation can be selected from the candidate parking paths as the target parking path, so that the automatic parking experience of the user is improved.
According to the technical scheme provided by the embodiment, a candidate parking path is determined according to vehicle position information in response to an automatic parking instruction; determining candidate parking information corresponding to the candidate parking path, and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space. According to the scheme, the problem that the steering wheel rotates too fast or the gear shifting times of the vehicle are too many to influence the parking experience of a driver in the automatic parking process when the vehicle automatically parks is solved. The method and the device have the advantages that when the vehicle automatically parks, the influence of steering wheel rudder rubbing times, parking gear shifting times and parking space attributes on the parking experience of the user in the vehicle parking process is fully considered, and a target parking path which can enable the user to have the best parking experience is automatically planned according to the candidate parking information through a parking path planning model, so that the automatic parking experience of the user is improved.
Example two
Fig. 2 is a flowchart of a parking path planning method provided by a second embodiment of the present invention, where the method is optimized based on the foregoing embodiment, and a preferred implementation manner of a parking path planning model for planning a parking path is provided by training a neural network model according to sample parking data in historical parking information and sample parking evaluation information corresponding to the sample parking data. Specifically, as shown in fig. 2, the method includes:
s210, determining sample parking data according to historical parking information of the vehicle.
The historical parking information includes: the method comprises the steps of historical parking steering wheel rudder rubbing times, historical parking gear shifting times, historical parking road conditions and historical parking space attributes of a vehicle in the historical parking process.
Specifically, the historical parking information of the vehicle can be randomly extracted, and the extracted historical parking information is used as sample parking data.
For example, the method for determining the sample parking data may be: and acquiring historical parking information of the vehicle, and determining candidate sample data from the historical parking information according to the information integrity of the historical parking information. And carrying out reliability analysis on the candidate sample data, and determining sample parking data from the candidate sample data according to an analysis result.
And if the historical parking information is recorded with complete parking parameter data, determining that the historical parking information is complete information. Correspondingly, if the data of the parking parameter data recorded in one historical parking information is missing, the historical parking information is incomplete information. The parking parameter data refer to the number of times of steering wheel steering of the vehicle in the history parking process, the number of times of gear shifting of the history parking, the road condition of the history parking and the attribute of the history parking space.
Specifically, according to the information integrity of the historical parking information, the historical parking information recorded with complete parking parameter data is determined to be candidate sample data. The method for reliability analysis of candidate sample data can be as follows: and determining the confidence of the sample data according to the candidate sample data, and determining the sample parking data from the candidate sample data according to the confidence of the sample data. For example, candidate sample data for which the sample data confidence is above a confidence threshold is determined to be sample parking data.
For example, the method for performing reliability analysis on candidate sample data to determine sample parking data may be: determining a historical vehicle parking heading angle corresponding to the candidate sample data, a historical left line distance between a historical vehicle parking position and a vehicle left parking space line, and a historical right line distance between the historical vehicle parking position and a vehicle right parking space line; and according to the historical vehicle parking heading angle and the historical distance difference value of the historical left-side line distance and the historical right-side line distance, carrying out reliability analysis on the candidate sample data, and determining sample parking data from the candidate sample data according to an analysis result.
The vehicle parking course angle refers to an angle between a vehicle body and parking space lines on two sides of a parking space where a vehicle is parked after the vehicle is parked. The parking space lines on two sides of the parking space refer to a parking space line on the left side of the vehicle and a parking space line on the right side of the vehicle.
Specifically, after the automatic parking of the vehicle corresponding to the candidate sample data is completed, the historical vehicle parking course angle of the vehicle in the historical parking space and the historical vehicle parking position of the vehicle in the historical parking space are read. And determining the historical left side line distance between the historical vehicle parking position and the vehicle left side parking space line and the historical right side line distance between the historical vehicle parking position and the vehicle right side parking space line according to the historical vehicle parking position. And according to the historical vehicle parking course angle and the historical distance difference value of the historical left-side line distance and the historical right-side line distance, carrying out reliability analysis on the candidate sample data to determine that the course angle condition is met, wherein the candidate sample data corresponding to the historical vehicle parking position with the historical distance difference value smaller than the difference value threshold value is the sample parking data.
It can be appreciated that determining sample parking data from historical parking information based on information integrity and data reliability may increase the validity of the sample parking data.
And S220, training the neural network model according to the sample parking data and sample parking evaluation information corresponding to the sample parking data, and determining a parking path planning model according to a training result.
The parking path planning model is used for evaluating the candidate parking paths according to the candidate parking information of the candidate parking paths and determining target parking paths from the candidate parking paths according to the evaluation result.
For example, the method for training the neural network model to determine the parking path planning model may be: determining model training data according to the sample parking data and sample parking evaluation information corresponding to the sample parking data, and dividing the model training data into training sample data and test sample data; training the neural network model according to training sample data, and determining a model to be verified; inputting test parking data in the test sample data into a model to be verified, and determining test evaluation information according to model output data of the model to be verified; and determining whether the model prediction accuracy of the model to be verified is greater than an accuracy threshold according to the test evaluation information and the test parking evaluation information in the test sample data, and if so, taking the model to be verified as a parking path planning model.
According to the scheme, the model training data are divided into the training sample data and the test sample data, the neural network model is trained according to the training sample data, and then the trained neural network model is subjected to model test according to the test sample, so that model parameters are adjusted according to test results, and the model accuracy of the parking path planning model can be improved.
S230, determining a candidate parking path according to the vehicle position information in response to the automatic parking instruction.
S240, determining candidate parking information corresponding to the candidate parking paths, and determining target parking paths according to a pre-trained parking path planning model and the candidate parking information.
The candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space.
According to the technical scheme of the embodiment, sample parking data is determined according to historical parking information of the vehicle. The historical parking information includes: the method comprises the steps of historical parking steering wheel rudder rubbing times, historical parking gear shifting times, historical parking road conditions and historical parking space attributes of a vehicle in the historical parking process; training the neural network model according to the sample parking data and sample parking evaluation information corresponding to the sample parking data, and determining a parking path planning model according to a training result; the parking path planning model is used for evaluating the candidate parking path according to the candidate parking information of the candidate parking path and determining a target parking path from the candidate parking path according to an evaluation result; determining a candidate parking path according to the vehicle position information in response to the automatic parking instruction; determining candidate parking information corresponding to the candidate parking path, and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space. According to the scheme, when the path planning model is determined, the influence of the steering wheel steering times and the parking shift times on the user parking experience is considered, the neural network model is trained according to the sample parking evaluation information, the candidate parking steering wheel steering times, the candidate parking shift times, the candidate parking road conditions and the candidate parking space attributes of the candidate parking spaces, so that the trained parking path planning model fully considers the parking experience of the user when planning the automatic parking path of the vehicle, the intelligent level of the parking path planning model is improved, and the model performance of the parking path planning model is improved.
Example III
Fig. 3 is a schematic structural diagram of a parking path planning apparatus according to a third embodiment of the present invention. The embodiment is applicable to the case of path planning of an automatic parking path of a vehicle. As shown in fig. 3, the parking path planning apparatus includes: a candidate parking path determination module 310 and a target parking path determination module 320.
Wherein, the candidate parking path determining module 310 is configured to determine a candidate parking path according to the vehicle location information in response to the automatic parking instruction;
the target parking path determining module 320 is configured to determine candidate parking information corresponding to the candidate parking path, and determine a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space.
According to the technical scheme provided by the embodiment, a candidate parking path is determined according to vehicle position information in response to an automatic parking instruction; determining candidate parking information corresponding to the candidate parking path, and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space. According to the scheme, the problem that the steering wheel rotates too fast or the gear shifting times of the vehicle are too many to influence the parking experience of a driver in the automatic parking process when the vehicle automatically parks is solved. The method and the device have the advantages that when the vehicle automatically parks, the influence of steering wheel rudder rubbing times, parking gear shifting times and parking space attributes on the parking experience of the user in the vehicle parking process is fully considered, and a target parking path which can enable the user to have the best parking experience is automatically planned according to the candidate parking information through a parking path planning model, so that the automatic parking experience of the user is improved.
Illustratively, the target parking path determination module 320 is specifically configured to:
determining candidate parking road conditions of the candidate parking paths;
according to the candidate parking road conditions of the candidate parking paths, the number of candidate parking steering wheel rudder rubbing times and the number of candidate parking gear shifting times when the vehicle parks based on the candidate parking paths are determined;
and taking the candidate parking steering wheel rudder rubbing times, the candidate parking gear shifting times, the candidate parking road conditions and the candidate parking space attributes of the candidate parking spaces as candidate parking information of the candidate parking paths.
Illustratively, the target parking path determination module 320 is further specifically configured to:
determining candidate path evaluation information of the candidate parking path according to the candidate parking information by adopting a pre-trained parking path planning model;
and determining target path evaluation information from the candidate path evaluation information through a parking path planning model, and taking a candidate parking path corresponding to the target path evaluation information as a target parking path.
The parking path planning device further includes:
the sample parking data determining module is used for determining sample parking data according to historical parking information of the vehicle; the historical parking information includes: the method comprises the steps of historical parking steering wheel rudder rubbing times, historical parking gear shifting times, historical parking road conditions and historical parking space attributes of a vehicle in the historical parking process;
the path planning model determining module is used for training the neural network model according to the sample parking data and sample parking evaluation information corresponding to the sample parking data, and determining a parking path planning model according to the training result; the parking path planning model is used for evaluating the candidate parking paths according to the candidate parking information of the candidate parking paths and determining target parking paths from the candidate parking paths according to the evaluation result.
Exemplary, a sample park data determination module includes:
the candidate sample data determining unit is used for acquiring historical parking information of the vehicle and determining candidate sample data from the historical parking information according to the information integrity of the historical parking information;
and the sample parking data determining unit is used for carrying out reliability analysis on the candidate sample data and determining the sample parking data from the candidate sample data according to the analysis result.
The sample parking data determination unit is, for example, specifically configured to:
determining a historical vehicle parking heading angle corresponding to the candidate sample data, a historical left line distance between a historical vehicle parking position and a vehicle left parking space line, and a historical right line distance between the historical vehicle parking position and a vehicle right parking space line;
and according to the historical vehicle parking heading angle and the historical distance difference value of the historical left-side line distance and the historical right-side line distance, carrying out reliability analysis on the candidate sample data, and determining sample parking data from the candidate sample data according to an analysis result.
Illustratively, the path planning model determination module is specifically configured to:
determining model training data according to the sample parking data and sample parking evaluation information corresponding to the sample parking data, and dividing the model training data into training sample data and test sample data;
training the neural network model according to training sample data, and determining a model to be verified;
inputting test parking data in the test sample data into a model to be verified, and determining test evaluation information according to model output data of the model to be verified;
and determining whether the model prediction accuracy of the model to be verified is greater than an accuracy threshold according to the test evaluation information and the test parking evaluation information in the test sample data, and if so, taking the model to be verified as a parking path planning model.
The parking path planning device provided by the embodiment can be applied to the parking path planning method provided by any embodiment, and has corresponding functions and beneficial effects.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. 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. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a parking path planning method.
In some embodiments, the parking path planning method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the parking path planning method described above may be performed when the computer program is loaded into the RAM 13 and executed by the processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the parking path planning method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device 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) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of parking path planning, comprising:
determining a candidate parking path according to the vehicle position information in response to the automatic parking instruction;
determining candidate parking information corresponding to the candidate parking path, and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space.
2. The method of claim 1, wherein determining candidate parking information corresponding to the candidate parking path comprises:
determining candidate parking road conditions of the candidate parking paths;
according to the candidate parking road conditions of the candidate parking paths, determining the number of candidate parking steering wheel rudder rubbing times and the number of candidate parking gear shifting times when the vehicle parks based on the candidate parking paths;
and taking the candidate parking steering wheel rudder rubbing times, the candidate parking gear shifting times, the candidate parking road conditions and the candidate parking space attributes of the candidate parking spaces as the candidate parking information of the candidate parking paths.
3. The method of claim 1, wherein determining a target parking path based on a pre-trained parking path planning model and the candidate parking information comprises:
determining candidate path evaluation information of the candidate parking path according to the candidate parking information by adopting a pre-trained parking path planning model;
and determining target path evaluation information from the candidate path evaluation information through the parking path planning model, and taking a candidate parking path corresponding to the target path evaluation information as a target parking path.
4. The method as recited in claim 1, further comprising:
determining sample parking data according to historical parking information of the vehicle; the historical parking information includes: the method comprises the steps of historical parking steering wheel rudder rubbing times, historical parking gear shifting times, historical parking road conditions and historical parking space attributes of a vehicle in the historical parking process;
training a neural network model according to the sample parking data and sample parking evaluation information corresponding to the sample parking data, and determining a parking path planning model according to a training result; the parking path planning model is used for evaluating the candidate parking path according to the candidate parking information of the candidate parking path and determining a target parking path from the candidate parking paths according to an evaluation result.
5. The method of claim 4, wherein determining sample parking data based on historical parking information for the vehicle comprises
Acquiring historical parking information of a vehicle, and determining candidate sample data from the historical parking information according to the information integrity of the historical parking information;
and carrying out reliability analysis on the candidate sample data, and determining sample parking data from the candidate sample data according to an analysis result.
6. The method of claim 5, wherein performing reliability analysis on the candidate sample data and determining sample parking data from the candidate sample data based on the analysis results comprises:
determining a historical vehicle parking course angle corresponding to the candidate sample data, and a historical left side line distance between a historical vehicle parking position and a vehicle left side parking space line and a historical right side line distance between the historical vehicle parking position and a vehicle right side parking space line;
and according to the historical vehicle parking course angle and the historical distance difference value of the historical left side line distance and the historical right side line distance, carrying out reliability analysis on the candidate sample data, and determining sample parking data from the candidate sample data according to an analysis result.
7. The method of claim 4, wherein training the neural network model based on the sample parking data and sample parking evaluation information corresponding to the sample parking data, and determining the parking path planning model based on the training result, comprises:
determining model training data according to the sample parking data and sample parking evaluation information corresponding to the sample parking data, and dividing the model training data into training sample data and test sample data;
training the neural network model according to the training sample data to determine a model to be verified;
inputting test parking data in the test sample data into the model to be verified, and determining test evaluation information according to model output data of the model to be verified;
and determining whether the model prediction accuracy of the model to be verified is greater than an accuracy threshold according to the test evaluation information and the test parking evaluation information in the test sample data, and if so, taking the model to be verified as a parking path planning model.
8. A parking path planning apparatus, comprising:
the candidate parking path determining module is used for determining a candidate parking path according to the vehicle position information in response to the automatic parking instruction;
the target parking path determining module is used for determining candidate parking information corresponding to the candidate parking path and determining a target parking path according to a pre-trained parking path planning model and the candidate parking information; the candidate parking information includes: the number of rubs of the candidate parking steering wheel, the number of candidate parking gear shifts, the candidate parking road condition and the candidate parking space attribute of the candidate parking space.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the parking path planning method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the parking path planning method according to any one of claims 1-7 when executed.
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