A data processing method, device, apparatus, and storage medium

Abstract

The present disclosure provides a data processing method and device, equipment and storage medium, relates to the field of artificial intelligence, specifically to the field of artificial intelligence, and specifically to the fields of automatic driving, intelligent transportation and machine learning. The specific implementation scheme is: determining a stay trajectory point from the driving trajectories of different vehicles; determining a second trajectory sequence according to the driving trajectory, the stay trajectory point, and the spatial index between the trajectory point and the parking lot contour; the second trajectory sequence is a trajectory sequence before entering the parking lot; determining a second road segment set corresponding to the second trajectory sequence based on the binding relationship between the driving trajectory and the road data; and determining a high-frequency road segment for entering the parking lot according to the second road segment set. Through the above technical scheme, the mining efficiency of the high-frequency road segment for entering the parking lot can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, specifically to the fields of autonomous driving, intelligent transportation, and machine learning. Background Technology

[0002] With the continuous development of intelligent navigation technology, more and more drivers are using navigation products to find parking lots. Parking lots, as an important service facility within navigation products, play a crucial role in improving drivers' travel experience and alleviating urban traffic congestion. However, the construction of parking lot data for navigation products also faces problems such as missing data, low data quality, and untimely data updates. Therefore, strengthening parking lot data construction and improving the quality and timeliness of parking lot information is of paramount importance to the development of intelligent navigation products. Summary of the Invention

[0003] This disclosure provides a data processing method, apparatus, device, and storage medium.

[0004] According to one aspect of this disclosure, a data processing method is provided, the method comprising:

[0005] Determine the stopping points from the driving trajectories of different vehicles;

[0006] A second trajectory sequence is determined based on the driving trajectory, the stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline; the second trajectory sequence is the trajectory sequence before entering the parking lot.

[0007] Based on the binding relationship between driving trajectory and road data, the second road segment set corresponding to the second trajectory sequence is determined;

[0008] Based on the second set of road segments, determine the high-frequency road segments leading to the parking lot.

[0009] According to another aspect of this disclosure, a data processing apparatus is provided, the apparatus comprising:

[0010] The stationary trajectory point determination module is used to determine stationary trajectory points from the driving trajectories of different vehicles;

[0011] The second trajectory sequence determination module is used to determine a second trajectory sequence based on the driving trajectory, the stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline; the second trajectory sequence is the trajectory sequence before entering the parking lot;

[0012] The second road segment set determination module is used to determine the second road segment set corresponding to the second trajectory sequence based on the binding relationship between the driving trajectory and road data.

[0013] The high-frequency road segment determination module is used to determine the high-frequency road segments entering the parking lot based on the second road segment set.

[0014] According to another aspect of this disclosure, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the data processing method described in any embodiment of this disclosure.

[0018] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to perform the data processing method described in any embodiment of this disclosure.

[0019] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the data processing method according to any embodiment of this disclosure.

[0020] According to the technology disclosed herein, the excavation efficiency of high-frequency road sections leading to parking lots can be improved.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0023] Figure 1 This is a flowchart of a data processing method provided according to an embodiment of the present disclosure;

[0024] Figure 2 This is a flowchart of another data processing method provided according to an embodiment of the present disclosure;

[0025] Figure 3 This is a flowchart of yet another data processing method provided according to an embodiment of the present disclosure;

[0026] Figure 4 This is a schematic diagram of the structure of a data processing apparatus provided according to an embodiment of the present disclosure;

[0027] Figure 5This is a block diagram of an electronic device used to implement the data processing method of the embodiments of this disclosure. Detailed Implementation

[0028] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Furthermore, it should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of driving trajectory and other related data involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0031] Among the many attributes of parking lot data, the parking lot entry route is undoubtedly the most critical, as the correct entry route is essential to ensuring that users' parking needs are met. However, due to the wide distribution of parking lot data and the difficulty of construction, there is currently a lack of large-scale methods for constructing parking lot entry routes. Therefore, this disclosure provides a method for mining high-frequency entry routes to parking lots based on driving trajectories.

[0032] Figure 1 This is a flowchart illustrating a data processing method according to an embodiment of this disclosure. The method is applicable to determining high-frequency road sections in a parking lot. The method can be executed by a data processing device, which can be implemented in software and / or hardware and integrated into an electronic device carrying data processing functions, such as a server. Figure 1 As shown, the data processing method in this embodiment may include:

[0033] S101, determine the stopping trajectory points from the driving trajectories of different vehicles.

[0034] In this embodiment, the driving trajectory refers to the driving trajectory of the vehicle acquired by different vehicle terminals; optionally, the driving trajectory includes multiple trajectory points, each trajectory point including information such as the terminal identifier of the vehicle terminal, vehicle model, instantaneous speed, location, and time. A stationary trajectory point refers to a trajectory point in the driving trajectory that maintains a constant position within a continuous time period; it should be noted that there are multiple stationary trajectory points.

[0035] One alternative approach involves acquiring the vehicle's entry trajectory into the parking lot from different vehicle-mounted terminals. Then, for each vehicle's trajectory, trajectory points belonging to the same predetermined period are sorted chronologically to obtain sorted trajectory points. For each sorted trajectory point, it is determined whether its position changes within a continuous time period. If not, this trajectory point is designated as a stationary trajectory point. The continuous time period can be set by those skilled in the art based on actual conditions, for example, one hour. It is understood that setting a continuous time period can exclude temporary parking situations, thus avoiding interference with the determination of subsequent high-frequency sections of the parking lot.

[0036] S102, determine the second trajectory sequence based on the driving trajectory, the stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline.

[0037] In this embodiment, the second trajectory sequence is the trajectory sequence before entering the parking lot; that is, a segment of the driving trajectory before entering the parking lot; optionally, it includes one or more stopping trajectory points, and a segment of trajectory sequence before the stopping trajectory points.

[0038] The spatial index between trajectory points and parking lot outlines refers to the spatial relationship between trajectory points within a parking lot and the parking lot outline. It can also be understood as the association between trajectory points and parking lot outlines, and is used to find the corresponding parking lot based on the trajectory points.

[0039] Specifically, based on the spatial index between the trajectory point and the parking contour, the parking lot associated with the stop trajectory point can be determined. Then, the first stop trajectory point to enter the parking lot can be determined from the stop trajectory points, and the stop trajectory point and the continuous trajectory of the driving trajectory before the stop trajectory point for a set time can be taken as the second trajectory sequence.

[0040] S103, based on the binding relationship between driving trajectory and road data, determine the second road segment set corresponding to the second trajectory sequence.

[0041] In this embodiment, the second road segment set refers to the road segments corresponding to the second trajectory sequence; optionally, the second road segment set includes one or more road segments.

[0042] The binding relationship between driving trajectory and road data refers to the correspondence after binding the driving trajectory and road data. The binding relationship can be represented by a line segment with direction to indicate the driving trajectory, and road markers on the line segment to indicate the road segment corresponding to the driving trajectory. Optionally, a preset road binding method can be used to bind the driving trajectory and road data to obtain the binding relationship between the driving trajectory and road data. In this embodiment, the preset road binding method is not specifically limited, and can be, for example, shortest distance matching, Hidden Markov Model, or machine learning.

[0043] Specifically, the second set of road segments corresponding to the second trajectory sequence can be found based on the binding relationship between the driving trajectory and road data.

[0044] S104, based on the second road segment set, determines the high-frequency road segments leading to the parking lot.

[0045] In this embodiment, high-frequency road sections refer to road sections where vehicles frequently travel when entering the parking lot; that is, road sections that vehicles frequently travel on when entering the parking lot. Furthermore, high-frequency road sections are used for navigation guidance in the parking lot.

[0046] Specifically, for different driving trajectories of the same vehicle and different driving trajectories of different vehicles, multiple sets of secondary road segments can be obtained to enter the parking lot. The number of times each road segment appears in the multiple sets of secondary road segments is counted, and the road segment with the most occurrences is taken as the high-frequency road segment to enter the parking lot.

[0047] The technical solution provided in this disclosure determines stopping trajectory points from the driving trajectories of different vehicles. Then, based on the driving trajectory, stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline, a second trajectory sequence is determined. Furthermore, based on the binding relationship between the driving trajectory and road data, a second road segment set corresponding to the second trajectory sequence is determined. Finally, based on the second road segment set, high-frequency road segments for entering the parking lot are determined. Compared to existing technologies that rely on on-site data collection or third-party cooperation to obtain parking lot data, which leads to high costs and poor timeliness, this disclosure determines high-frequency road segments for entering the parking lot solely through different driving trajectories. This accurately identifies road segments for entering the parking lot, especially when there are multiple parking lot entrances or when the parking lot lacks a fixed entrance, enabling rapid and accurate identification of these road segments, thus facilitating navigation.

[0048] Based on the above embodiments, as an optional method of this disclosure, determining the stationary trajectory point from the driving trajectories of different vehicles includes: determining continuous trajectory points from the driving trajectories of different vehicles; and determining whether the continuous trajectory point is a stationary trajectory point based on the instantaneous speed and position of the continuous trajectory point.

[0049] Among them, continuous trajectory points refer to trajectory points that are continuous in time and related in location within the driving trajectory.

[0050] Specifically, the driving trajectory points of the same vehicle entering the parking lot within the same time period are sorted in chronological order to obtain continuous trajectory points. Then, for each continuous trajectory point, it is determined whether the instantaneous speed of the continuous trajectory point is 0 for a continuous set time period, and whether the position corresponding to the continuous trajectory point remains almost unchanged, i.e., the range of position change is within the set range. If so, the continuous trajectory point is determined as the corresponding parking trajectory point of the parking lot. It should be noted that the set time period can be set by those skilled in the art according to the actual situation, such as 1 hour; the set range can be set by those skilled in the art according to the actual situation, such as 5 meters.

[0051] Understandably, determining the stopping trajectory point based on the trajectory point status, i.e., speed and position, makes the determination of the stopping trajectory point more accurate, thereby providing a guarantee for the subsequent excavation of high-frequency road sections entering the parking lot.

[0052] Figure 2 This is a flowchart of another data processing method provided according to an embodiment of this disclosure. Based on the above embodiments, this embodiment further optimizes the process of "determining a second trajectory sequence based on the driving trajectory, stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline," providing an optional implementation scheme. For example... Figure 2 As shown, the data processing method in this embodiment may include:

[0053] S201, determine the stopping trajectory points from the driving trajectories of different vehicles.

[0054] S202, determine the first trajectory sequence based on the driving trajectory and the stopping trajectory points.

[0055] In this implementation, the first trajectory sequence is the trajectory sequence before parking, which includes multiple trajectory points.

[0056] In one alternative approach, for each driving trajectory, if there are multiple stopping trajectory points, the first stopping trajectory point with the earliest time is selected according to the time sequence, along with the continuous trajectory points in the driving trajectory preceding the stopping trajectory point for a set time period, as the first trajectory sequence.

[0057] S203, Based on the spatial index between the trajectory point and the parking lot outline, determine the parking lot corresponding to the stopping trajectory point.

[0058] Specifically, the parking lot corresponding to the stop trajectory point can be determined based on the spatial index between the trajectory point and the parking lot outline, using the stop trajectory point as the index.

[0059] S204. Determine the second trajectory sequence based on the first trajectory sequence and the positional relationship between the stopping trajectory points and the parking lot.

[0060] In this embodiment, the second trajectory sequence is the trajectory sequence before entering the parking lot; it includes multiple trajectory points.

[0061] The so-called positional relationship between the stop trajectory point and the parking lot includes whether the stop trajectory point is inside the parking lot or outside the parking lot.

[0062] Specifically, based on a preset location determination method, the positional relationship between the stopping trajectory point and the parking lot can be determined. Then, the first trajectory sequence is traversed in chronological order. The first stopping trajectory point entering the parking lot is determined from the first trajectory sequence. Using this stopping trajectory point as a dividing point, the trajectory sequence before the dividing point is extracted from the first trajectory sequence to obtain the second trajectory sequence.

[0063] S205, based on the binding relationship between driving trajectory and road data, determines the second road segment set corresponding to the second trajectory sequence.

[0064] S206, based on the second road segment set, determines the high-frequency road segments leading to the parking lot.

[0065] The technical solution provided in this disclosure determines stopping trajectory points from the driving trajectories of different vehicles. Then, based on the driving trajectories and stopping trajectory points, a first trajectory sequence is determined. Based on the spatial index between the trajectory points and the parking lot outline, the parking lot corresponding to the stopping trajectory point is determined. Next, based on the first trajectory sequence and the positional relationship between the stopping trajectory point and the parking lot, a second trajectory sequence is determined. Then, based on the binding relationship between the driving trajectory and road data, a second road segment set corresponding to the second trajectory sequence is determined. Finally, based on the second road segment set, the high-frequency road segments for entering the parking lot are determined. This technical solution, through the spatial index between trajectory points and the parking lot outline, and the binding relationship between the driving trajectory and road data, can quickly and accurately determine the second road segment set, thus laying the foundation for subsequently determining the high-frequency road segments for entering the parking lot.

[0066] Based on the above embodiments, as an optional method of this disclosure, determining a first trajectory sequence according to the driving trajectory and the stopping trajectory points includes: determining a first stopping point from the stopping trajectory points; and extracting continuous trajectory points of a first duration from the driving trajectory based on the first stopping point as the first trajectory sequence.

[0067] The first stopping point is the trajectory point where the vehicle finally stops in the parking lot.

[0068] Specifically, based on the time corresponding to each stop trajectory point, the first stop point can be determined from the stop trajectory points according to the time sequence, that is, the stop trajectory point corresponding to the last moment; then the first stop point and the continuous trajectory points in the driving trajectory before the first stop point for a first duration are regarded as the first trajectory sequence. The first duration can be set by those skilled in the art according to the actual situation, such as 5 minutes.

[0069] Understandably, preprocessing the driving trajectory yields the first trajectory sequence before parking, and removing some useless trajectories from the driving trajectory facilitates the rapid determination of the second trajectory sequence.

[0070] Based on the above embodiments, as an optional method of this disclosure, determining the second trajectory sequence according to the first trajectory sequence and the positional relationship between the stop trajectory points and the parking lot includes: determining the second stop point from the stop trajectory points according to the positional relationship between the stop trajectory points and the parking lot; and using the second stop point to extract the first trajectory sequence to obtain the second trajectory sequence.

[0071] The second stop point is the first stop trajectory point for entering the parking lot.

[0072] Specifically, based on the positional relationship between the stop trajectory points and the parking lot, the stop trajectory points within the parking lot can be determined. Based on the time of the stop trajectory points, the second stop point can be determined from the stop trajectory points within the parking lot. Then, using the second stop point as the dividing point, the trajectory sequence between the second stop points in the first trajectory sequence can be extracted as the second trajectory sequence.

[0073] Understandably, by finding the first stopping point upon entering the parking lot and extracting the first trajectory sequence, the second trajectory sequence can be obtained quickly and in real time.

[0074] Based on the above embodiments, as an optional method of this disclosure, it further includes: determining the outline of the parking lot; and establishing a spatial index between the trajectory points and the parking lot outline based on the trajectory points and parking lot data.

[0075] Among them, the parking lot outline refers to the planar shape characteristics of the parking lot.

[0076] Specifically, the parking lot outline can be obtained based on satellite image recognition; simultaneously, a large number of trajectory points can be acquired; then, a spatial index can be established between the trajectory points and the parking lot outline based on the trajectory points and parking lot data. It should be noted that this embodiment does not specifically limit the spatial indexing method; it can be a custom latitude and longitude indexing method, such as GeoHash, R-tree, etc.

[0077] Understandably, providing a way to construct a spatial index between trajectory points and parking lot outlines can facilitate quick matching of corresponding parking lots based on stop trajectory points.

[0078] Figure 3 This is a flowchart of another data processing method provided according to an embodiment of this disclosure. Based on the above embodiments, this embodiment further optimizes the step of "determining the second road segment set corresponding to the second trajectory sequence based on the binding relationship between the driving trajectory and road data," providing an optional implementation scheme. For example... Figure 3 As shown, the data processing method in this embodiment may include:

[0079] S301 determines the stopping trajectory points from the driving trajectories of different vehicles.

[0080] S302, determine the first trajectory sequence based on the driving trajectory and the stopping trajectory points.

[0081] S303, based on the spatial index between the trajectory point and the parking lot outline, determine the parking lot corresponding to the stopping trajectory point.

[0082] S304. Determine the second trajectory sequence based on the first trajectory sequence and the positional relationship between the stopping trajectory points and the parking lot.

[0083] The second trajectory sequence is the trajectory sequence before entering the parking lot;

[0084] S305, based on the binding relationship between driving trajectory and road data, determines the first road segment set corresponding to the first trajectory sequence.

[0085] In this embodiment, the first road segment set refers to the road segments corresponding to the first trajectory sequence.

[0086] Specifically, based on the binding relationship between driving trajectory and road data, the first trajectory sequence can be used as an index to find and determine the first road segment set corresponding to the first trajectory sequence.

[0087] S306, Based on the second trajectory sequence, determine the second segment set corresponding to the second trajectory sequence from the first segment set.

[0088] Specifically, the second trajectory sequence can be used as an index to determine the second segment set corresponding to the second trajectory sequence from the first segment set.

[0089] S307, based on the second road segment set, determines the high-frequency road segments leading to the parking lot.

[0090] The technical solution provided in this disclosure determines stopping trajectory points from the driving trajectories of different vehicles. Then, based on the driving trajectories and stopping trajectory points, a first trajectory sequence is determined. Based on the spatial index between the trajectory points and the parking lot outline, the parking lot corresponding to the stopping trajectory point is determined. Next, based on the first trajectory sequence and the positional relationship between the stopping trajectory point and the parking lot, a second trajectory sequence is determined. Then, based on the binding relationship between driving trajectories and road data, a first road segment set corresponding to the first trajectory sequence is determined. Based on the second trajectory sequence, a second road segment set corresponding to the second trajectory sequence is determined from the first road segment set. Finally, based on the second road segment set, high-frequency road segments for entering the parking lot are determined. This technical solution, by determining the second road segment set based on the first road segment set determined by the first trajectory sequence, can improve the efficiency of determining the second road segment set.

[0091] Based on the above embodiments, as an optional method of this disclosure, determining the high-frequency road segments for entering the parking lot according to the second road segment set includes: splitting the second road segment set into road segments according to road signs to obtain at least one road segment; associating the parking lot with the road segment to obtain the association relationship between the parking lot and the single road segment; and statistically analyzing the frequency of occurrence of the association relationship between the parking lot and the single road segment within a set time period to obtain the high-frequency road segments for entering the parking lot. The set time period can be determined by those skilled in the art based on actual circumstances.

[0092] Among them, road signs refer to data used to uniquely identify roads, such as road names.

[0093] Specifically, the second road segment set can be split into road segments according to road identifiers to obtain at least one road segment. Then, parking lots and each road segment can be associated one by one to obtain the association relationship between parking lots and single road segments. For example, the parking lot is park_id, the second road set is (link_1, link_2, link_3, ..., link_n), where n is a positive integer; the association relationships between parking lots and single road segments are (park_id, link_1), (park_id, link_2), (park_id, link_3), ..., (park_id, link_n).

[0094] Furthermore, the relationship between parking lots and individual road segments within a defined time period is determined, and then the frequency of occurrence of the relationship between each parking lot and individual road segment is counted. For example, the frequency of occurrence of the relationship between parking lots and individual road segments is as follows:

[0095] (park_1, link_1), 1;

[0096] (park_1, link_2), 1;

[0097] (park_1, link_1), 1;

[0098] (park_1, link_1), 1;

[0099] (park_2,link_3), 1.

[0100] The final statistical results showing the correlation between parking lots and individual road segments are as follows:

[0101] (park_1, link_1), 3;

[0102] (park_1,link_2), 1.

[0103] (park_2,link_3), 1.

[0104] Finally, the most frequently occurring road segment is designated as the high-frequency road segment for entering the parking lot. In the example above, the high-frequency road segment for parking lot park_1 is link_1, and the high-frequency road segment for parking lot park_2 is link_3.

[0105] It is understandable that providing a method for identifying high-frequency road sections would provide accurate guidance for subsequent navigation.

[0106] Figure 4 This is a schematic diagram of a data processing apparatus according to an embodiment of the present disclosure. The embodiments of the present disclosure are applicable to situations involving the determination of high-frequency road sections in parking lots. The apparatus can be implemented in software and / or hardware and can be integrated into an electronic device that carries data processing functions, such as a server. Figure 4 As shown, the data processing device 400 may include:

[0107] The stationary trajectory point determination module 401 is used to determine stationary trajectory points from the driving trajectories of different vehicles;

[0108] The second trajectory sequence determination module 402 is used to determine the second trajectory sequence based on the driving trajectory, the stopping trajectory points, and the spatial index between the trajectory points and the outline of the parking lot; the second trajectory sequence is the trajectory sequence before entering the parking lot;

[0109] The second road segment set determination module 403 is used to determine the second road segment set corresponding to the second trajectory sequence based on the binding relationship between the driving trajectory and road data.

[0110] The high-frequency road segment determination module 404 is used to determine the high-frequency road segments entering the parking lot based on the second road segment set.

[0111] The technical solution provided in this disclosure determines stopping trajectory points from the driving trajectories of different vehicles. Then, based on the driving trajectory, stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline, a second trajectory sequence is determined. Furthermore, based on the binding relationship between the driving trajectory and road data, a second road segment set corresponding to the second trajectory sequence is determined. Finally, based on the second road segment set, high-frequency road segments for entering the parking lot are determined. Compared to existing technologies that rely on on-site data collection or third-party cooperation to obtain parking lot data, which leads to high costs and poor timeliness, this disclosure determines high-frequency road segments for entering the parking lot solely through different driving trajectories. This accurately identifies road segments for entering the parking lot, especially when there are multiple parking lot entrances or when the parking lot lacks a fixed entrance, enabling rapid and accurate identification of these road segments, thus facilitating navigation.

[0112] Furthermore, the dwell trajectory point determination module 401 is specifically used for:

[0113] Determine continuous trajectory points from the driving trajectories of different vehicles;

[0114] Based on the instantaneous velocity and position of the continuous trajectory points, determine whether the continuous trajectory points are stationary trajectory points.

[0115] Furthermore, the second trajectory sequence determination module 402 includes:

[0116] The first trajectory sequence determination unit is used to determine the first trajectory sequence based on the driving trajectory and the stopping trajectory points;

[0117] The parking lot determination unit is used to determine the parking lot corresponding to the stopping trajectory point based on the spatial index between the trajectory point and the parking lot outline;

[0118] The second trajectory sequence determination unit is used to determine the second trajectory sequence based on the first trajectory sequence and the positional relationship between the stopping trajectory point and the parking lot.

[0119] Furthermore, the first trajectory sequence determination unit is specifically used for:

[0120] The first stopping point is determined from the stopping trajectory points; the first stopping point is the trajectory point where the vehicle stops at the end of the driving trajectory.

[0121] Based on the first stop point, extract continuous trajectory points of the first duration from the driving trajectory to form the first trajectory sequence.

[0122] Furthermore, the second trajectory sequence determination unit is specifically used for:

[0123] Based on the positional relationship between the stop trajectory points and the parking lot, determine the second stop point from the stop trajectory points; the second stop point is the first stop trajectory point entering the parking lot;

[0124] By using the second stop point, the first trajectory sequence is truncated to obtain the second trajectory sequence.

[0125] Furthermore, the second segment set determination module 403 is specifically used for:

[0126] Based on the binding relationship between driving trajectory and road data, the first road segment set corresponding to the first trajectory sequence is determined;

[0127] Based on the second trajectory sequence, determine the second segment set corresponding to the second trajectory sequence from the first segment set.

[0128] Furthermore, the high-frequency segment determination module 404 is specifically used for:

[0129] According to the road signs, the second road segment set is divided into road segments to obtain at least one road segment;

[0130] By associating parking lots with road segments, the relationship between parking lots and individual road segments can be obtained;

[0131] The frequency of the correlation between parking lots and individual road segments within a set time period is counted to obtain the high-frequency road segments for entering parking lots.

[0132] Furthermore, the device also includes:

[0133] The parking lot outline determination module is used to determine the outline of the parking lot.

[0134] The spatial index building module is used to build a spatial index between trajectory points and parking lot outlines based on trajectory points and parking lot data.

[0135] Furthermore, high-frequency road sections are used for navigation guidance in parking lots.

[0136] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0137] Figure 5 This is a block diagram of an electronic device used to implement the data processing method of the embodiments of this disclosure. Figure 5A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0138] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0139] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0140] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as data processing methods. For example, in some embodiments, the data processing method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform data processing methods by any other suitable means (e.g., by means of firmware).

[0141] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0144] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the 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 can be received in any form (including sound input, voice input, or tactile input).

[0145] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0146] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0147] Artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies mainly include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0148] Cloud computing refers to a technology system that enables access to a shared pool of physical or virtual resources via a network. These resources can include servers, operating systems, networks, software, applications, and storage devices, and can be deployed and managed on demand and in a self-service manner. Cloud computing technology can provide efficient and powerful data processing capabilities for applications such as artificial intelligence and blockchain, as well as for model training.

[0149] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0150] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A data processing method, comprising: Determine the stopping points from the driving trajectories of different vehicles; A second trajectory sequence is determined based on the driving trajectory, the stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline; the second trajectory sequence is the trajectory sequence before entering the parking lot; the spatial index refers to the spatial relationship between the trajectory points in the parking lot and the parking lot outline; Based on the binding relationship between driving trajectory and road data, the second road segment set corresponding to the second trajectory sequence is determined; the binding relationship refers to the correspondence after binding driving trajectory and road data. Based on the second set of road segments, determine the high-frequency road segments leading to the parking lot.

2. The method according to claim 1, wherein, Determining the stopping trajectory point from the driving trajectories of different vehicles includes: Determine continuous trajectory points from the driving trajectories of different vehicles; Based on the instantaneous velocity and position of the continuous trajectory points, determine whether the continuous trajectory points are stationary trajectory points.

3. The method according to claim 1, wherein, Determining the second trajectory sequence based on the driving trajectory, the stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline includes: Based on the driving trajectory and the stopping trajectory points, a first trajectory sequence is determined; Based on the spatial index between the trajectory point and the parking lot outline, the parking lot corresponding to the stopping trajectory point is determined; A second trajectory sequence is determined based on the first trajectory sequence and the positional relationship between the stopping trajectory point and the parking lot.

4. The method according to claim 3, wherein, Determining the first trajectory sequence based on the driving trajectory and the stationary trajectory points includes: A first stopping point is determined from the stopping trajectory points; the first stopping point is the trajectory point at the end of the driving trajectory when the vehicle stops; Based on the first stop point, a continuous trajectory point of a first duration is extracted from the driving trajectory to form a first trajectory sequence.

5. The method according to claim 3, wherein, Determining the second trajectory sequence based on the first trajectory sequence and the positional relationship between the stopping trajectory point and the parking lot includes: Based on the positional relationship between the stopping trajectory points and the parking lot, a second stopping point is determined from the stopping trajectory points; the second stopping point is the first stopping trajectory point entering the parking lot; Using the second stop point, the first trajectory sequence is truncated to obtain the second trajectory sequence.

6. The method according to claim 3, wherein, The determination of the second road segment set corresponding to the second trajectory sequence based on the binding relationship between the driving trajectory and road data includes: Based on the binding relationship between driving trajectory and road data, the first road segment set corresponding to the first trajectory sequence is determined; Based on the second trajectory sequence, determine the second road segment set corresponding to the second trajectory sequence from the first road segment set.

7. The method according to claim 1, wherein, The step of determining the high-frequency road segments entering the parking lot based on the second road segment set includes: According to the road signs, the second road segment set is divided into road segments to obtain at least one road segment; The parking lot and the road segment are associated to obtain the association relationship between the parking lot and the single road segment; The frequency of the correlation between parking lots and individual road segments within a set time period is counted to obtain the high-frequency road segments leading to the parking lot.

8. The method according to any one of claims 1-7, further comprising: Determine the outline of the parking lot; Based on trajectory points and parking lot data, a spatial index is established between trajectory points and parking lot outlines.

9. The method according to any one of claims 1-7, wherein, The high-frequency road section is used for navigation guidance in parking lots.

10. A data processing apparatus, comprising: The stationary trajectory point determination module is used to determine stationary trajectory points from the driving trajectories of different vehicles; The second trajectory sequence determination module is used to determine a second trajectory sequence based on the driving trajectory, the stopping trajectory points, and the spatial index between the trajectory points and the parking lot outline; the second trajectory sequence is the trajectory sequence before entering the parking lot; the spatial index refers to the spatial relationship between the trajectory points in the parking lot and the parking lot outline; The second road segment set determination module is used to determine the second road segment set corresponding to the second trajectory sequence based on the binding relationship between the driving trajectory and road data; the binding relationship refers to the correspondence after binding the driving trajectory and road data. The high-frequency road segment determination module is used to determine the high-frequency road segments entering the parking lot based on the second road segment set.

11. The apparatus according to claim 10, wherein, The dwell trajectory point determination module is specifically used for: Determine continuous trajectory points from the driving trajectories of different vehicles; Based on the instantaneous velocity and position of the continuous trajectory points, determine whether the continuous trajectory points are stationary trajectory points.

12. The apparatus according to claim 10, wherein, The second trajectory sequence determination module includes: The first trajectory sequence determination unit is used to determine a first trajectory sequence based on the driving trajectory and the stopping trajectory points; The parking lot determination unit is used to determine the parking lot corresponding to the stopping trajectory point based on the spatial index between the trajectory point and the parking lot outline; The second trajectory sequence determination unit is used to determine the second trajectory sequence based on the first trajectory sequence and the positional relationship between the stopping trajectory point and the parking lot.

13. The apparatus according to claim 12, wherein, The first trajectory sequence determination unit is specifically used for: A first stopping point is determined from the stopping trajectory points; the first stopping point is the trajectory point at the end of the driving trajectory when the vehicle stops; Based on the first stop point, a continuous trajectory point of a first duration is extracted from the driving trajectory to form a first trajectory sequence.

14. The apparatus according to claim 12, wherein, The second trajectory sequence determination unit is specifically used for: Based on the positional relationship between the stopping trajectory points and the parking lot, a second stopping point is determined from the stopping trajectory points; the second stopping point is the first stopping trajectory point entering the parking lot; Using the second stop point, the first trajectory sequence is truncated to obtain the second trajectory sequence.

15. The apparatus according to claim 12, wherein, The second segment set determination module is specifically used for: Based on the binding relationship between driving trajectory and road data, the first road segment set corresponding to the first trajectory sequence is determined; Based on the second trajectory sequence, determine the second road segment set corresponding to the second trajectory sequence from the first road segment set.

16. The apparatus according to claim 10, wherein, The high-frequency segment determination module is specifically used for: According to the road signs, the second road segment set is divided into road segments to obtain at least one road segment; The parking lot and the road segment are associated to obtain the association relationship between the parking lot and the single road segment; The frequency of the correlation between parking lots and individual road segments within a set time period is counted to obtain the high-frequency road segments leading to the parking lot.

17. The apparatus according to any one of claims 10-16, further comprising: The parking lot outline determination module is used to determine the outline of the parking lot. The spatial index building module is used to build a spatial index between trajectory points and parking lot outlines based on trajectory points and parking lot data.

18. The apparatus according to any one of claims 10-16, wherein, The high-frequency road section is used for navigation guidance in parking lots.

19. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the data processing method according to any one of claims 1-9.

20. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the data processing method according to any one of claims 1-9.

21. A computer program product comprising a computer program that, when executed by a processor, implements the data processing method according to any one of claims 1-9.

Images