CN114528365A

CN114528365A - Method and device for identifying parking area on highway, electronic equipment and medium

Info

Publication number: CN114528365A
Application number: CN202210158513.XA
Authority: CN
Inventors: 李壮; 葛德金; 李曼; 谷艳蕾; 卢振; 夏德国; 曹婷婷; 杨建忠
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-02-21
Filing date: 2022-02-21
Publication date: 2022-05-24

Abstract

The invention provides a method and a device for identifying a parking area on a highway, electronic equipment and a medium, and relates to the technical field of computers, in particular to the technical field of intelligent transportation, big data and artificial intelligence. The implementation scheme is as follows: the method comprises the steps of obtaining a target area and a target driving track of a vehicle passing through the target area, wherein the target driving track comprises a plurality of track points, each track point in the plurality of track points has speed information and direction information, and the target driving track comprises a plurality of continuous track points, wherein the speed information in the target area is smaller than a first threshold value; and judging whether the target area is a highway parking area or not based on the speed information and the direction information of the plurality of track points.

Description

Method and device for identifying parking area on highway, electronic equipment and medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the field of intelligent transportation, big data, and artificial intelligence technologies, and in particular, to a method and an apparatus for identifying a parking area on a highway, an electronic device, a computer-readable storage medium, and a computer program product.

Background

An electronic map (digital map) is a map that is digitally stored and referred to using computer technology. Various types of map elements are drawn on the electronic map, such as roads, shopping malls, schools, hospitals, landmark buildings, and the like.

The self-driving trip is the main mode of transportation. During the self-driving trip, the user can use the terminal device (such as a mobile phone, a tablet computer, a vehicle-mounted navigation device, and the like) equipped with the electronic map application to perform route navigation. The electronic map application can plan at least one navigation route according to the departure place and the destination specified by the user, and estimate the driving time of each navigation route. The user can drive along the navigation route selected by the user so as to smoothly reach the destination.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.

Disclosure of Invention

The present disclosure provides a method and apparatus for identifying a parking area on a highway, an electronic device, a computer-readable storage medium, and a computer program product.

According to an aspect of the present disclosure, there is provided an identification method of a parking area of a highway, including: the method comprises the steps of obtaining a target area and a target driving track of a vehicle passing through the target area, wherein the target driving track comprises a plurality of track points, each track point in the plurality of track points has speed information and direction information, and the target driving track comprises a plurality of continuous track points, wherein the speed information in the target area is smaller than a first threshold value; and judging whether the target area is a highway parking area or not based on the speed information and the direction information of the plurality of track points.

According to an aspect of the present disclosure, there is provided a first acquisition module configured to acquire a target area and a target travel track of a vehicle passing through the target area, the target travel track including a plurality of track points, each of the plurality of track points having speed information and direction information, the target travel track including a plurality of consecutive track points located within the target area, the speed information of which is less than a first threshold; and the judging module is configured to judge whether the target area is an expressway parking area or not based on the speed information and the direction information of the plurality of track points.

According to an aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the method.

According to an aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the above-described method.

According to an aspect of the disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the above-described method.

According to one or more embodiments of the present disclosure, the parking areas of the expressway can be automatically, efficiently and accurately identified, so that the accuracy of the electronic map and the navigation of the user is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

fig. 2 shows a flow chart of an identification method of a parking area of a highway according to an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of obtaining a vehicle travel track according to an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of a target area and a corresponding target travel trajectory according to an embodiment of the present disclosure;

fig. 5 shows a schematic diagram of an identification process of a service area according to an embodiment of the present disclosure;

fig. 6 is a block diagram illustrating a structure of an identification apparatus of a parking area of a highway according to an embodiment of the present disclosure; and

FIG. 7 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

In the disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the related users are all in accordance with the regulations of related laws and regulations, and do not violate the customs of the public order.

Self-driving travel is the main mode of travel. During the self-driving trip, the user can use the terminal device (such as a mobile phone, a tablet computer, a vehicle-mounted navigation device, and the like) equipped with the electronic map application to perform route navigation. The electronic map application can plan at least one navigation route according to the starting position and the ending position input by the user and estimate the driving time of each navigation route. The user can drive along the navigation route selected by the user so as to smoothly reach the destination.

The navigation route often includes a highway. Parking areas in highways, such as service areas capable of providing comprehensive services of parking, refueling, catering, supermarkets, lodging and the like, and small parking areas, large parking lots and the like which only provide parking services, are places for users to park and rest, and have important influence on driving experience of the users.

The method is used for accurately describing the parking area of the expressway in the electronic map, and is a premise for accurately planning a navigation route and accurately predicting the time of the route. The loss of the data of the expressway parking area in the electronic map can cause navigation route planning errors or estimated time length errors, and the user experience is seriously influenced. Therefore, timely identification of newly added highway parking areas in the road network is required.

In the related art, manual means such as vehicle collection and user feedback are usually adopted to collect information such as images and videos of the expressway, and then the newly added expressway parking area is identified manually. The method has the advantages of large workload, long time consumption and low efficiency. In other related techniques, satellite images may also be acquired, and new freeway parking areas may be identified from the satellite images. However, the accuracy of satellite images acquired by the current civil satellite is low, and meanwhile, due to overhead shooting, only two-dimensional plane images can be obtained, the identifiability is poor, and newly-added expressway parking areas cannot be accurately identified.

Therefore, the embodiment of the disclosure provides an identification method for a parking area on an expressway, which can automatically, efficiently and accurately identify the parking area on the expressway, thereby improving the accuracy of an electronic map and user navigation.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or

more client devices

101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120.

Client devices

101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the identification method of the highway parking area to be performed.

In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of

client devices

101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user operating a

client device

101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The user may navigate using

client devices

101, 102, 103, 104, 105, and/or 106. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices

101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various Mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular telephones, smart phones, tablets, Personal Digital Assistants (PDAs), and the like. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), Short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, Wi-Fi), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the

client devices

101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

101, 102, 103, 104, 105, and 106.

In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the conventional physical host and Virtual Private Server (VPS) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as music files. The database 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The database 130 may be of different types. In certain embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the databases in response to the commands.

In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or conventional stores supported by a file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

For purposes of the disclosed embodiments, an electronic map application may be included in the

client devices

101, 102, 103, 104, 105, and 106 in the example of fig. 1, which may provide various electronic map-based functions, such as online navigation, offline route planning, location finding, and the like. Accordingly, the server 120 may be a server corresponding to the electronic map application. A service program may be included in the server 120, and the service program may provide a map service to an electronic map application program running in the client device based on the electronic map data already stored in the database 130. Alternatively, the server 120 may also provide the electronic map data to a client device, with an electronic map application running in the client device providing a map service based on locally stored electronic map data.

Specifically, the server 120 or the

client devices

101, 102, 103, 104, 105, and 106 may execute the method for identifying a parking area on a highway according to the embodiment of the present disclosure, identify a newly added parking area on a highway in a road network in time, and add the newly added parking area to the electronic map, so that the data of the electronic map is more accurate, thereby improving the accuracy of user navigation and improving user experience.

Fig. 2 shows a flow chart of a method 200 of identifying a parking area of a highway according to an embodiment of the present disclosure. Method 200 may be performed at a server (e.g., server 120 shown in fig. 1) or may be performed at a client device (e.g.,

client devices

101, 102, 103, 104, 105, and 106 shown in fig. 1). That is, the execution subject of each step of the method 200 may be the server 120 shown in fig. 1, or may be the

client devices

101, 102, 103, 104, 105, and 106 shown in fig. 1.

As shown in fig. 2, the method 200 includes:

step 210, obtaining a target area and a target driving track of a vehicle passing through the target area, wherein the target driving track comprises a plurality of track points, each track point in the plurality of track points has speed information and direction information, and the target driving track comprises a plurality of continuous track points, the speed information of which is smaller than a first threshold value, in the target area; and

and step 220, judging whether the target area is a highway parking area or not based on the speed information and the direction information of the plurality of track points.

According to the embodiment of the disclosure, the expressway parking area can be automatically identified based on the driving track of the vehicle, and the efficiency, the real-time performance and the accuracy of identifying the expressway parking area are improved.

The various steps of method 200 are described in detail below.

In step 210, a target area and a target travel track of a vehicle passing through the target area are obtained, the target travel track including a plurality of track points, each of the plurality of track points having speed information and direction information, and the target travel track including a plurality of consecutive track points located within the target area where the speed information is less than a first threshold.

It should be understood that during the running of the vehicle, the position information of the vehicle at different moments can be acquired by a satellite positioning module (which can be an on-board satellite positioning module, and can also be a satellite positioning module in a terminal device used by a user in the vehicle), and the position information at different moments can be stored locally in the vehicle or sent to a server. The position information of the vehicle at different times forms the travel trajectory of the vehicle. The driving track of the vehicle comprises a plurality of track points, each track point corresponds to a specific time and has corresponding position information.

The position information of the track points is used to indicate the position of the vehicle at the corresponding time. The position information may be, for example, coordinates such as longitude and latitude coordinates, UTM (Universal Transverse Mercator grid system) coordinates, and the like.

In the embodiment of the disclosure, in addition to the position information of the vehicle at different times, the position information of the vehicle at different times is acquired through the satellite positioning module, and information of the speed, the direction and the like of the vehicle at different times is acquired through other sensor modules and is stored locally or transmitted to the server together with the position information of the vehicle. Accordingly, each track point in the vehicle travel track has speed information and direction information in addition to position information.

The speed information of the track points is used for indicating the running speed of the vehicle at the corresponding moment. The unit of the speed information is usually km/h or m/s.

The direction information of the track points is used to indicate the driving direction of the vehicle at the corresponding time. The direction information may be expressed as an angle of a traveling direction of the vehicle with respect to a reference direction. For example, the east direction is taken as a reference direction, the angle increases along the counterclockwise direction, and the value range of the direction information is 0-360 degrees. If the vehicle runs in the north direction, the direction information is 90 degrees; if the vehicle is traveling southwest, the direction information is 225 °.

Fig. 3 shows a schematic diagram of acquiring a vehicle travel track according to an embodiment of the present disclosure. As shown in fig. 3, the user may navigate a route through a map application in a terminal device (e.g., a mobile phone, a tablet, a vehicle-mounted device, etc.) or other applications (e.g., a meal order application, a logistics application, etc.) embedded in a map sdk (software Development kit), and drive a vehicle along the navigated route. During the running process of the vehicle, the terminal device uploads the collected information of the position, the speed, the direction and the like to the server 320 through the network 310. In addition, the cooperative user vehicle (e.g., a contract vehicle of a taxi-taking platform) and the panoramic view capturing vehicle also capture information such as position, speed, direction, etc. in real time during driving, and upload the information to the server 320 through the network 310. Based on the position, speed, direction, etc. of the vehicle at different times, the travel track of the vehicle can be formed.

In the embodiment of the present disclosure, the target area is any area to be determined whether or not it is a parking area of an expressway. The target travel track is a travel track of the vehicle passing through the target area, and the target travel track has a parking track segment in the target area, that is, has a plurality of continuous track points whose speed information is smaller than the first threshold value. The first threshold value is typically a small value, e.g. 0m/s, 0.5m/s, etc.

FIG. 4 shows a schematic diagram of a target area and a corresponding target travel trajectory according to an embodiment of the present disclosure. In the embodiment shown in fig. 4, the target area is an area 410 located on one side of a highway 420. The travel locus 430 of the vehicle is a target travel locus corresponding to the target area 410, and the arrow in the figure indicates the travel direction of the vehicle. The target travel track 430 passes through the target area 410. Target travel trajectory 430 includes trajectory points a-N and includes three consecutive trajectory points G-I having a velocity of 0 (i.e., less than a first threshold) that are located within target area 410. And the track points G-I form a parking track segment.

According to some embodiments, the target area may be obtained as follows step 230-250:

step 230, obtaining a plurality of vehicle running tracks, wherein each vehicle running track comprises a plurality of track points, and each track point has speed information and position information;

step 240, determining a plurality of parking track segments from the plurality of vehicle driving tracks based on the speed information of each track point in the plurality of vehicle driving tracks, wherein each parking track segment comprises a plurality of continuous track points of which the speed information is smaller than the first threshold value; and

and step 250, determining at least one candidate area based on the position information of each track point in the plurality of parking track segments, wherein the target area is any one of the at least one candidate area.

According to the embodiment, a small number of candidate areas can be mined from a large number of position areas in a road network, so that the subsequent calculation amount is greatly reduced, and the calculation efficiency and the real-time performance of the expressway parking area identification are improved.

According to some embodiments, in step 230, a plurality of vehicle travel trajectories within an area (e.g., country, province, city, etc.) where the map data update is to be performed may be acquired.

According to some embodiments, in step 240, a parking trajectory segment in each vehicle driving trajectory may be extracted separately. It will be appreciated that a vehicle travel track may include only one or more (i.e., at least two) parking track segments, or may not include a parking track segment. After the parking track segment corresponding to each vehicle driving track is obtained, the parking track segments of each vehicle driving track can be merged (union set) to obtain a plurality of parking track segments.

According to some embodiments, step 250 may further include the following steps 252-258:

step 252, clustering the track points in the plurality of parking track segments based on the position information of each track point in the plurality of parking track segments to obtain a plurality of clusters;

step 254, determining the class cluster with the number of the included parking track segments larger than a third threshold value as a target class cluster;

256, determining a position area where the target cluster is located based on the position information of each track point in the target cluster; and

step 258, the location area is determined as a candidate area.

According to the embodiment, the position areas with a large amount of parking behaviors gathered can be rapidly excavated through clustering, the candidate areas of suspected expressway parking areas (including service areas, parking lots and the like) are obtained, and the accuracy and the efficiency of expressway parking area identification are improved.

According to some embodiments, in step 252, a density clustering algorithm may be employed to cluster the trajectory points in the plurality of docked trajectory segments. The Density Clustering algorithm may be, for example, a DBSCAN (Density-Based Clustering of Application with Noise) algorithm. By clustering, a plurality of class clusters can be obtained.

In step 254, the number of parking track segments included in each cluster may be counted, and the cluster in which the number of parking track segments included is greater than a third threshold (e.g., 100) is determined as the target cluster of the suspected expressway parking area. Specifically, the parking track segments corresponding to each track point in each cluster type may be obtained, and the parking track segments corresponding to each track point are merged (union set), so as to obtain a set of the parking track segments corresponding to the cluster type. Then, by counting the number of elements in the set, the number of the stationary track segments included in each cluster class can be obtained.

After determining at least one candidate region through step 250 above, any one of these candidate regions may be used as the target region to be identified in step 210.

Further, according to some embodiments, a vehicle travel track corresponding to a longest parking track segment in the target area may be taken as the target travel track, where the longest parking track segment is the parking track segment including the largest number of track points.

According to the above-described embodiment, the parking trajectory segment corresponding to the target travel trajectory is the parking trajectory segment including the largest number of trajectory points in the target area, and thus the target travel trajectory is the most representative vehicle travel trajectory passing through the target area. The expressway parking area is identified based on the target driving track, and the identification efficiency can be improved on the premise of ensuring the identification accuracy.

In step 220, it is determined whether the target area is a parking area of the expressway based on the speed information and the direction information of the plurality of track points.

According to some embodiments, step 220 further comprises the following steps 222-226:

step 222, extracting the speed change trend of the target driving track in the target area based on the speed information of the plurality of track points;

step 224, extracting a direction change trend of the target driving track in the target area based on the direction information of the plurality of track points; and

and step 226, judging whether the target area is an expressway parking area or not at least based on the speed change trend and the direction change trend.

When a vehicle enters/leaves a parking area of a highway, the change trend of the speed and the direction has certain rules. Therefore, according to the above embodiment, automatic and accurate recognition of the parking areas on the expressway can be realized based on the variation tendency of the speed and the direction.

According to some embodiments, for step 226, the target area may be determined to be a highway parking area in response to determining that the speed trend and the direction trend satisfy the following condition:

the method comprises the following steps that 1, the speed change trend is that the speed change trend is reduced from a first preset value to a first threshold value, and the speed change trend is increased from the first threshold value to a second preset value;

in condition 2, the direction change tendency changes.

The speed information of the vehicle during the driving into and out of the highway parking area generally conforms to the trend of the change of "speed reduction → parking → starting", and the trend of the direction information (angle) is generally not a single value (e.g., only decreases, or only increases), but a plurality of values, i.e., the trend of the change is changed (e.g., first decreases, then increases, and then decreases). Therefore, when the above-described condition 1 and condition 2 are simultaneously satisfied, the target area is determined to be the expressway parking area, whereby the expressway parking area can be quickly and accurately identified.

Since the driving speed of the vehicle on the expressway is high and the driving speed on the urban and rural roads is low, by reasonably setting the first preset value and the second preset value in the condition 1 (for example, setting the first preset value and the second preset value as high values), parking scenes irrelevant to the expressway, such as urban parking lots, rural fields, and the like, can be effectively filtered, and thus, the parking area of the expressway can be accurately identified. It should be understood that the first preset value and the second preset value may be the same or different.

For a parking area on a highway, vehicles usually have continuous driving-in, parking and driving-out behaviors in a short time, namely, the vehicles firstly drive into the parking area from a highway section and stay in the parking area for a period of time and then drive into the highway section from the parking area, and the driving is reflected as the change of a change trend on direction information, namely the direction change trend has a plurality of values. For example, referring to fig. 4, if the east direction is the reference direction and the counterclockwise direction is the direction in which the direction information increases, the direction change trend of the target driving trajectory 430 in the target area 410 is: decreasing, increasing, and then decreasing. For the parking behavior of vehicles in the urban traffic light area, the direction information of the vehicles usually has only a single change trend (such as turning right, going straight and the like), and the change of the change trend does not occur. Therefore, by the condition 2, parking scenes such as urban traffic lights and the like irrelevant to the expressway can be effectively filtered, so that the expressway parking area can be accurately identified.

According to further embodiments, step 220 may further include the following step 228:

step 228, based on the direction information of the plurality of track points, extracting the maximum direction difference of the target driving track in the target area, where the maximum direction difference is the difference between the maximum direction information and the minimum direction information in the target area.

Accordingly, for step 226, the target area may be determined to be a highway parking area in response to determining that the speed trend, the direction trend, and the maximum direction difference satisfy the following conditions:

condition 2, the direction change trend is changed;

condition 3, the maximum direction difference is greater than the second threshold.

Conditions 1 and 2 can be as described above and will not be described herein.

The range of variation of the directional information of a vehicle is generally large during the driving into and out of the parking area of the highway. The vehicle usually has a small variation range of direction information during the course of driving from the main road to the sub-road of the expressway and driving back to the main road from the sub-road. Therefore, by appropriately setting the second threshold value in condition 3 (for example, the second threshold value may be set to 30 °), it is possible to effectively filter a parking scene in which the vehicle "drives from the main road to the sub road, temporarily stops at the sub road, and drives back from the sub road" so as to accurately identify the parking area on the expressway.

Fig. 5 shows a schematic diagram of an identification process 500 of a highway service area according to an embodiment of the present disclosure. As shown in fig. 5, the process 500 includes steps 501-508.

In step 501, a large amount of track information is acquired, and the acquired track is preprocessed to improve the quality of the track, so as to improve the accuracy of service area identification. The preprocessing may include, for example, trajectory denoising (removing trajectories with GPS drift), trajectory smoothing, trajectory thinning, and so on. The trajectories acquired and processed in step 501 include not only the driving trajectories of vehicles, but also trajectories of other vehicles such as motorcycles and bicycles, trajectories of pedestrians, and the like.

In step 502, the travel trajectory of the vehicle is extracted from the mass trajectories obtained in step 501. The extraction of the vehicle driving trajectory can be realized by using a trained neural network model, for example. Specifically, the input of the neural network model is a characteristic of the trajectory (for example, an average speed, a variance of the speed, a distribution of the speed, and the like of the trajectory), and the output is a result of determination as to whether the trajectory is a vehicle travel trajectory.

In step 503, an area to be subjected to map data update, such as province, city, etc., is acquired.

In step 504, a large number of vehicle driving tracks in the area are obtained, a parking track segment is extracted from the vehicle driving tracks, and then density clustering is performed on each track point in the parking track segment to obtain a plurality of clusters.

In step 505, it is determined whether the number of docked track segments included in the current class cluster reaches a threshold. If not, go to step 506 to obtain the next class cluster, and then continue to go to step 505. If yes, go to step 507.

In step 507, the position area where the current cluster is located is taken as a target area to be identified, the longest track (i.e. the vehicle running track corresponding to the longest parking track segment) in the area is taken as a target running track, the speed change trend and the direction change trend of the longest parking track segment are extracted, and whether the target area is a service area is judged based on the speed change trend and the direction change trend.

In step 508, if it is determined that the target area is a service area, the electronic map data is updated based on the service area so that the electronic map data can include the latest and most accurate service area information.

The method for identifying the parking area of the expressway in the embodiment of the disclosure has the following advantages:

1. the timeliness is high. The track of the user is continuously reported to the server every hour and every minute, and the timeliness for identifying the parking area of the expressway can be improved to a day level, an hour level or even a minute level through track mining, so that real-time identification is realized.

2. The cost is low. Only the track needs to be excavated, and the panoramic acquisition does not need to be carried out manually.

3. The accuracy is high. Because the track information of the user is the real feedback of the road elements, the road elements of the parking area of the expressway can be effectively excavated through the accurate judgment of the track characteristics.

According to the embodiment of the disclosure, an identification device of a parking area of a highway is also provided. Fig. 6 shows a block diagram of a structure of an identification apparatus 300 for a parking area of a highway according to an embodiment of the present disclosure. As shown in fig. 6, the apparatus 600 includes:

a first obtaining module 610 configured to obtain a target area and a target travel track of a vehicle passing through the target area, the target travel track including a plurality of track points, each of the plurality of track points having speed information and direction information, the target travel track including a plurality of continuous track points located within the target area, the speed information of which is less than a first threshold; and

and the judging module 620 is configured to judge whether the target area is an expressway parking area or not based on the speed information and the direction information of the plurality of track points.

According to some embodiments, the determining module 620 comprises: a first extraction unit configured to extract a speed variation tendency of the target travel locus within the target area based on speed information of the plurality of locus points; a second extraction unit configured to extract a direction change tendency of the target travel locus within the target area based on direction information of the plurality of locus points; and a determination unit configured to determine whether the target area is a highway parking area based on at least the speed change tendency and the direction change tendency.

According to some embodiments, the determination unit is further configured to determine that the target area is a highway parking area in response to determining that the speed variation tendency and the direction variation tendency satisfy the following condition: the speed change trend is decreased from a first preset value to the first threshold value and increased from the first threshold value to a second preset value; and there is a change in the direction change tendency.

According to some embodiments, the determining module 620 further comprises: a third extraction unit configured to extract a maximum direction difference of the target travel locus within the target area based on direction information of the plurality of track points, the maximum direction difference being a difference between the maximum direction information and minimum direction information within the target area, and the determination unit is further configured to determine that the target area is an expressway parking area in response to determining that the speed change tendency, the direction change tendency, and the maximum direction difference satisfy the following conditions: the speed variation trend is decreased from a first preset value to the first threshold value and increased from the first threshold value to a second preset value; the direction change trend is changed; and the maximum direction difference is greater than a second threshold.

According to some embodiments, the apparatus 600 further comprises: a second acquisition module configured to acquire a plurality of vehicle travel trajectories, each vehicle travel trajectory including a plurality of trajectory points, each trajectory point having speed information and position information; a first determination module configured to determine a plurality of parking trajectory segments from the plurality of vehicle travel trajectories based on speed information of each trajectory point in the plurality of vehicle travel trajectories, each parking trajectory segment including a plurality of consecutive trajectory points for which the speed information is less than the first threshold; a second determining module configured to determine at least one candidate region based on position information of each track point in the plurality of docked track segments, wherein the target region is any one of the at least one candidate region.

According to some embodiments, the second determining module comprises: the clustering unit is configured to cluster the track points in the plurality of parking track segments based on the position information of the track points in the plurality of parking track segments to obtain a plurality of clusters; a first determining unit configured to determine, as a target class cluster, a class cluster in which the number of included stationary track segments is greater than a third threshold; the second determining unit is configured to determine a position area where the target cluster is located based on the position information of each track point in the target cluster; and a third determination unit configured to determine the location area as the candidate area.

According to some embodiments, the target travel track is a vehicle travel track corresponding to a longest parking track segment in the target area, and the longest parking track segment is a parking track segment including the largest number of track points.

It should be understood that the various modules or units of the apparatus 600 shown in fig. 6 may correspond to the various steps in the method 200 described with reference to fig. 2. Thus, the operations, features and advantages described above with respect to the method 200 are equally applicable to the apparatus 600 and the modules and units comprised thereby. Certain operations, features and advantages may not be described in detail herein for the sake of brevity.

Although specific functionality is discussed above with reference to particular modules, it should be noted that the functionality of the various modules discussed herein can be separated into multiple modules and/or at least some of the functionality of multiple modules can be combined into a single module. For example, the first obtaining module 610 and the determining module 620 described above may be combined into a single module in some embodiments.

It should also be appreciated that various techniques may be described herein in the general context of software, hardware elements, or program modules. The various modules described above with respect to fig. 6 may be implemented in hardware or in hardware in combination with software and/or firmware. For example, the modules may be implemented as computer program code/instructions configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, the modules may be implemented as hardware logic/circuitry. For example, in some embodiments, one or more of the modules 610, 620 may be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip (which includes one or more components of a Processor (e.g., a Central Processing Unit (CPU), microcontroller, microprocessor, Digital Signal Processor (DSP), etc.), memory, one or more communication interfaces, and/or other circuitry), and may optionally execute received program code and/or include embedded firmware to perform functions.

According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.

Referring to fig. 7, a block diagram of a structure of an electronic device 1000, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the electronic device 700 includes a computing unit 701, which may perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM702, and the RAM703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

A number of components in the electronic device 700 are connected to the I/O interface 705, including: an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. The input unit 706 may be any type of device capable of inputting information to the device 700, and the input unit 706 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote controller. Output unit 707 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 708 may include, but is not limited to, magnetic or optical disks. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and/or a chipset, such as bluetooth^TMDevices, 802.11 devices, Wi-Fi devices, WiMAX devices, cellular communication devices, and/or the like.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 701 performs the various methods and processes described above, such as the method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM702 and/or communications unit 709. When the computer program is loaded into RAM703 and executed by the computing unit 701, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method 200 by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. A method of identifying a parking area on a highway, comprising:

the method comprises the steps of obtaining a target area and a target driving track of a vehicle passing through the target area, wherein the target driving track comprises a plurality of track points, each track point in the plurality of track points has speed information and direction information, and the target driving track comprises a plurality of continuous track points, wherein the speed information in the target area is smaller than a first threshold value; and

and judging whether the target area is a highway parking area or not based on the speed information and the direction information of the plurality of track points.

2. The method of claim 1, wherein determining whether the target area is a highway parking area based on the speed information and the direction information of the plurality of track points comprises:

extracting the speed change trend of the target driving track in the target area based on the speed information of the track points;

extracting the direction change trend of the target driving track in the target area based on the direction information of the plurality of track points; and

and judging whether the target area is an expressway parking area or not at least based on the speed change trend and the direction change trend.

3. The method of claim 2, wherein determining whether the target area is a highway parking area based on at least the speed trend and the direction trend comprises:

in response to determining that the speed change trend and the direction change trend satisfy the following conditions, determining that the target area is an expressway parking area:

the speed change trend is decreased from a first preset value to the first threshold value and increased from the first threshold value to a second preset value; and

there is a change in the direction change tendency.

4. The method of claim 2, further comprising:

extracting a maximum direction difference of the target driving track in the target area based on the direction information of the plurality of track points, wherein the maximum direction difference is a difference between the maximum direction information and the minimum direction information in the target area,

wherein determining whether the target area is a highway parking area based on at least the speed change trend and the direction change trend comprises:

determining the target area as a highway parking area in response to determining that the speed change trend, the direction change trend, and the maximum direction difference satisfy the following conditions:

the direction change trend is changed; and

the maximum direction difference is greater than a second threshold.

5. The method of any of claims 1-4, further comprising:

acquiring a plurality of vehicle running tracks, wherein each vehicle running track comprises a plurality of track points, and each track point has speed information and position information;

determining a plurality of parking trajectory segments from the plurality of vehicle driving trajectories based on the speed information of each trajectory point in the plurality of vehicle driving trajectories, each parking trajectory segment including a plurality of consecutive trajectory points for which the speed information is less than the first threshold; and

and determining at least one candidate region based on the position information of each track point in the plurality of parking track segments, wherein the target region is any one of the at least one candidate region.

6. The method of claim 5, wherein determining at least one candidate region based on the position information for each track point in the plurality of docked track segments comprises:

clustering the track points in the plurality of parking track segments based on the position information of each track point in the plurality of parking track segments to obtain a plurality of clusters;

determining the class clusters with the number of the included parking track segments larger than a third threshold value as target class clusters;

determining a position area where the target class cluster is located based on the position information of each track point in the target class cluster; and

determining the location area as the candidate area.

7. The method according to claim 5 or 6, wherein the target travel track is a vehicle travel track corresponding to a longest parking track segment in the target area, and the longest parking track segment is a parking track segment with the largest number of track points.

8. An identification device for a parking area on a highway, comprising:

the vehicle driving system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is configured to acquire a target area and a target driving track of a vehicle passing through the target area, the target driving track comprises a plurality of track points, each track point in the plurality of track points has speed information and direction information, and the target driving track comprises a plurality of continuous track points, the speed information of which is smaller than a first threshold value, and the continuous track points are located in the target area; and

and the judging module is configured to judge whether the target area is an expressway parking area or not based on the speed information and the direction information of the plurality of track points.

9. The apparatus of claim 8, wherein the means for determining comprises:

a first extraction unit configured to extract a speed variation tendency of the target travel locus within the target area based on speed information of the plurality of locus points;

a second extraction unit configured to extract a direction change tendency of the target travel locus within the target area based on direction information of the plurality of locus points; and

a determination unit configured to determine whether the target area is a highway parking area based on at least the speed change tendency and the direction change tendency.

10. The apparatus of claim 9, wherein the determination unit is further configured to determine the target area as a highway parking area in response to determining that the speed trend and the direction trend meet the following condition:

there is a change in the direction change tendency.

11. The apparatus of claim 9, wherein the means for determining further comprises:

a third extraction unit configured to extract a maximum direction difference of the target travel locus within the target area based on direction information of the plurality of track points, the maximum direction difference being a difference between the maximum direction information and the minimum direction information within the target area, and wherein,

the determination unit is further configured to determine that the target area is an expressway parking area in response to determining that the speed change tendency, the direction change tendency, and the maximum direction difference satisfy the following conditions:

the direction change trend is changed; and

the maximum direction difference is greater than a second threshold.

12. The apparatus of any of claims 8-11, further comprising:

a second acquisition module configured to acquire a plurality of vehicle travel tracks, wherein each vehicle travel track comprises a plurality of track points, and each track point has speed information and position information;

a first determination module configured to determine a plurality of parking trajectory segments from the plurality of vehicle travel trajectories based on speed information of each trajectory point in the plurality of vehicle travel trajectories, each parking trajectory segment including a plurality of consecutive trajectory points for which the speed information is less than the first threshold; and

a second determining module configured to determine at least one candidate region based on position information of each track point in the plurality of docked track segments, wherein the target region is any one of the at least one candidate region.

13. The apparatus of claim 12, wherein the second determining means comprises:

the clustering unit is configured to cluster the track points in the plurality of parking track segments based on the position information of the track points in the plurality of parking track segments to obtain a plurality of clusters;

a first determining unit configured to determine, as a target class cluster, a class cluster in which the number of included stationary track segments is greater than a third threshold;

the second determining unit is configured to determine a position area where the target cluster is located based on the position information of each track point in the target cluster; and

a third determination unit configured to determine the location area as the candidate area.

14. The apparatus according to claim 11 or 12, wherein the target travel track is a vehicle travel track corresponding to a longest parking track segment in the target area, the longest parking track segment being a parking track segment including the largest number of track points.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-7.

17. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-7 when executed by a processor.