Intersection image generation method, device, computer equipment and computer program product

Abstract

The application is suitable for the field of intelligent driving technology, and provides a kind of intersection portrait generation method, device, computer equipment and computer program product, method includes: the vehicle trajectory of each lane of target intersection in preset period is clustered, the driving mode of each lane is determined, the vehicle quantity of each lane is counted, the statistical probability corresponding to the driving mode of each lane is determined according to the vehicle quantity of each lane, according to the driving mode of each lane of target intersection and the statistical probability corresponding to the driving mode of each lane, determine intersection portrait.The intersection portrait provides rich environmental information for autonomous vehicle, including lane usage and traffic flow, these information can dynamically reflect the traffic condition of intersection, help autonomous driving system better adapt to complex traffic environment, improve the accuracy and safety of decision-making.

Description

Technical Field

[0001] This application belongs to the field of intelligent driving technology, and in particular relates to a method, device, computer equipment and computer program product for generating intersection images. Background Technology

[0002] Traditional intersection profiling methods primarily involve conducting traffic flow surveys at intersections through manual observation or the use of traffic counters to collect traffic flow data for each lane at different times. The collected data is then used to build a traffic flow model and analyze the distribution and changing patterns of traffic flow. The analysis results are then presented to form an intersection profile. However, these methods rely on on-site surveys and manual analysis, which may result in untimely data updates. Furthermore, intersection traffic conditions are constantly changing, leading to low accuracy and effectiveness of the analytical results obtained from these methods. Summary of the Invention

[0003] This application provides a method, apparatus, computer device, and computer program product for generating intersection profiles, which can solve the technical problems that the generation of existing intersection profiles relies on on-site investigation and manual analysis, data updates may not be timely, and the accuracy and effectiveness of the generated intersection profiles are not high.

[0004] In a first aspect, embodiments of this application provide a method for generating intersection profiles, including:

[0005] Cluster the vehicle trajectories of each lane at the target intersection within a preset time period to determine the driving mode of each lane.

[0006] Count the number of vehicles in each lane;

[0007] The statistical probability of the driving mode for each lane is determined based on the number of vehicles in each lane.

[0008] Based on the driving patterns of each lane at the target intersection and the statistical probabilities corresponding to each driving pattern, an intersection profile is determined.

[0009] In one possible implementation of the first aspect, the method further includes:

[0010] Obtain the lane attributes for each lane;

[0011] The step of clustering vehicle trajectories in each lane of a target intersection within a preset time period to determine the driving mode of each lane includes:

[0012] Based on the lane attributes of each lane, the vehicle trajectories of each lane are clustered to determine the driving mode of each lane.

[0013] In one possible implementation of the first aspect, the clustering of vehicle trajectories in each lane based on lane attributes to determine the driving mode of each lane includes:

[0014] Based on the lane attributes of each lane, the vehicle trajectories of each lane are clustered to obtain the clustered trajectories of each lane.

[0015] The clustered trajectories are analyzed based on the lane attributes of each lane to identify and remove abnormal trajectories.

[0016] Based on the clustered trajectories after removing outliers, the driving mode of each lane is determined.

[0017] In one possible implementation of the first aspect, the number of vehicles in each lane includes the total number of vehicles in each lane and the number of vehicles corresponding to each driving mode in each lane.

[0018] The determination of the statistical probability corresponding to the driving mode of each lane based on the number of vehicles in each lane includes:

[0019] The ratio of the number of vehicles corresponding to each driving mode in each lane to the total number of vehicles in each lane is used as the statistical probability of each driving mode in each lane.

[0020] In one possible implementation of the first aspect, the method further includes:

[0021] When the target vehicle is driving at the target intersection, the intersection image is sent to the terminal device associated with the target vehicle.

[0022] In one possible implementation of the first aspect, the step of sending the intersection image to a terminal device associated with the target vehicle when the target vehicle is traveling at the target intersection includes:

[0023] When the target vehicle is traveling at the target intersection at the current time, the intersection profile for the preset time period prior to the current time is sent to the terminal device.

[0024] Secondly, embodiments of this application provide an intersection image generation device, comprising:

[0025] The driving mode determination module is used to cluster the vehicle trajectories of each lane at the target intersection within a preset time period to determine the driving mode of each lane.

[0026] The statistics module is used to count the number of vehicles in each lane;

[0027] The probability determination module is used to determine the statistical probability corresponding to the driving mode of each lane based on the number of vehicles in each lane.

[0028] The intersection profile determination module is used to determine the intersection profile based on the driving patterns of each lane at the target intersection and the statistical probabilities corresponding to the driving patterns of each lane.

[0029] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intersection portrait generation method described in any one of the first aspects above.

[0030] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the intersection portrait generation method described in any one of the first aspects above.

[0031] Fifthly, embodiments of this application provide a computer program product that, when run on a computer device, causes the computer device to execute the intersection image generation method described in any one of the first aspects.

[0032] The beneficial effects of the embodiments in this application compared with the prior art are:

[0033] By clustering vehicle trajectories across lanes at a target intersection within a preset time period, typical driving patterns for different lanes can be identified. This analysis helps to refine the understanding of traffic flow distribution and behavior at the intersection. Statistical analysis of the number of vehicles in each lane and determination of the statistical probability corresponding to each lane's driving pattern reveals the frequency and likelihood of each driving pattern across different time periods, aiding in the prediction of intersection traffic conditions within a specific timeframe. Combining the driving patterns and corresponding statistical probabilities of each lane allows for the construction of an intersection profile. This profile provides autonomous vehicles with rich environmental information, including lane usage and traffic flow. This information dynamically reflects the intersection's traffic conditions, helping autonomous driving systems better adapt to complex traffic environments and improving the accuracy and safety of decision-making.

[0034] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1This is a schematic flowchart of an intersection image generation method provided in an embodiment of this application;

[0037] Figure 2 This is a schematic flowchart of an intersection portrait generation method provided in another embodiment of this application;

[0038] Figure 3 This is a schematic diagram of the intersection image generation device provided in the embodiments of this application;

[0039] Figure 4 This is a schematic diagram of the structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0040] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0041] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0042] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0043] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0044] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0046] Figure 1 A schematic flowchart of an intersection portrait generation method provided in an embodiment of this application is shown.

[0047] S101, cluster the vehicle trajectories of each lane at the target intersection within a preset time period to determine the driving mode of each lane.

[0048] The preset time period refers to a time period pre-set for analyzing intersection profiles (including vehicle driving patterns). This time period can be determined and adjusted according to actual traffic conditions or needs.

[0049] The target intersection is the intersection for which a portrait needs to be generated.

[0050] Vehicle trajectory refers to the trajectory data formed by multiple vehicles within a certain time and space range. This trajectory data can reveal what kind of trajectory (such as straight-ahead trajectory) and driving pattern (straight-ahead pattern) multiple vehicles form in a specific area or road segment.

[0051] Lane driving modes refer to the expected driving behavior of vehicles within a lane in accordance with traffic rules and road design. These modes are typically related to the function of the lane and the indication of traffic lights, defining the path that vehicles should follow when passing through an intersection. Driving modes include, but are not limited to: straight-ahead mode, left-turn mode, right-turn mode, and U-turn mode. A single lane may correspond to one or more driving modes.

[0052] Straight-ahead mode: refers to the behavior of vehicles traveling directly forward along the lane.

[0053] Left turn mode: refers to the behavior of a vehicle turning from the current lane to the left lane.

[0054] Right turn mode: refers to the behavior of a vehicle turning from the current lane to the right lane.

[0055] U-turn mode: refers to the behavior of a vehicle making a 180-degree turn at an intersection or in a specific area to return to its original direction.

[0056] One approach is to use density-based clustering to cluster the vehicle trajectories of each lane at the target intersection.

[0057] In this embodiment, vehicle trajectory data for each lane at the target intersection is collected within a preset time period. Cluster analysis is then performed on the collected vehicle trajectory data. The purpose of cluster analysis is to group vehicles with similar travel paths together to identify different driving patterns. Based on the clustering results, the primary driving patterns of vehicles in each lane are determined. For example, it can be identified which lanes are primarily used for straight-ahead travel and which lanes are primarily used for left or right turns.

[0058] To illustrate this, consider a busy intersection in a city. We'll analyze traffic conditions during the morning rush hour (8:00-9:00 AM). During this time, we collect vehicle trajectory data for each lane. Analysis reveals that most vehicles in lane 1 proceed straight through the intersection, classifying it as a "straight-through mode." Many vehicles in lane 2 slow down and turn left as they approach the intersection, classifying it as a "left-turn mode." Vehicles in lane 3 slow down and turn right as they approach the intersection, classifying it as a "right-turn mode." Vehicles in lane 4 typically wait at the stop line before following the vehicle in front, classifying it as a "straight-through mode." This approach not only reveals the primary driving patterns of each lane but also allows for optimization of traffic signal control, improving intersection efficiency, reducing congestion, and even providing more accurate guidance for autonomous vehicles.

[0059] In one optional embodiment, the lane attributes of each lane are obtained, and the vehicle trajectories of each lane are clustered based on the lane attributes of each lane to determine the driving mode of each lane.

[0060] Lane attributes define the rules for vehicles to travel within a lane. Lane attributes can include, but are not limited to, the following categories: straight lane, left-turn lane, right-turn lane, U-turn lane, etc. A lane can correspond to one or more lane attributes.

[0061] In the clustering process, known lane attributes can be used as prior knowledge to guide the process. For example, if a lane is defined as a straight-ahead lane and a right-turn lane, the clustering algorithm can expect that the vehicle trajectories in this lane will mainly exhibit these two driving modes (straight-ahead mode and right-turn mode), meaning the clustering algorithm primarily clusters vehicles according to these two driving modes. This prior knowledge helps the clustering algorithm better distinguish between normal vehicle trajectories and abnormal trajectories (such as noise). For instance, a trajectory that clearly indicates a left turn in a straight-ahead lane might be considered noise.

[0062] In one optional embodiment, the step of clustering vehicle trajectories of each lane based on lane attributes to determine the driving mode of each lane specifically includes:

[0063] The vehicle trajectories of each lane are clustered based on the lane attributes of each lane to obtain the clustered trajectory of each lane; the clustered trajectory is analyzed based on the lane attributes of each lane to identify and remove abnormal trajectories; and the driving mode of each lane is determined based on the clustered trajectory after removing abnormal trajectories.

[0064] The process of determining the driving mode for each lane involves two lane attribute determinations.

[0065] The first integration of lane attributes is used to obtain clustering trajectories. Its purpose is to guide the clustering process of vehicle trajectories in the initial stage of clustering. Since each lane typically has a predefined driving direction (i.e., lane attribute), the clustering algorithm can use this information to anticipate and identify driving patterns that conform to the lane attribute. The aim of this integration is to utilize lane attributes as prior knowledge to help the clustering algorithm better distinguish and identify normal driving patterns in vehicle trajectories, thereby improving the accuracy and efficiency of clustering.

[0066] The second integration of lane attributes is for the analysis and removal of abnormal trajectories. Its purpose is to: after obtaining the clustering results, lane attributes are again used to analyze the clustered trajectories to identify and remove abnormal trajectories (such as trajectories where signals disappear or illegal trajectories). Abnormal trajectories refer to those that do not conform to the expected driving patterns of the lane attributes; for example, obvious left or right turns in a straight lane. The purpose of this integration is to further refine the clustering results, ensuring the removal of abnormal trajectories that may be caused by errors, special circumstances, or other atypical behaviors, thereby improving the accuracy and reliability of driving pattern recognition.

[0067] By combining lane attributes twice, the expected driving patterns can be extracted more accurately from vehicle trajectory data, while reducing the impact of errors and noise. The first combination ensures that the clustering process proceeds according to the intended use of the lanes, while the second combination validates and optimizes the clustering results, ensuring that the final determined driving patterns are accurate and reliable. This dual validation mechanism helps improve the quality and effectiveness of the entire clustering and driving pattern recognition process.

[0068] S102, count the number of vehicles in each lane.

[0069] The vehicle count for each lane represents the number of vehicles that passed through each lane within the aforementioned preset time period. This vehicle count for each lane includes the total number of vehicles in each lane and the number of vehicles corresponding to each driving mode within each lane. Specifically, for each lane, the total number of all vehicles passing through that lane within the preset time period is counted. Within each lane, the number of vehicles belonging to different driving modes (e.g., straight-ahead mode, left-turn mode, right-turn mode) is further counted. This can be achieved by analyzing clustering results, where each cluster represents a driving mode.

[0070] S103, determine the statistical probability corresponding to the driving mode of each lane based on the number of vehicles in each lane.

[0071] In this embodiment, a statistical probability for each driving mode is calculated based on the total number of vehicles in each lane and the number of vehicles corresponding to each driving mode. Specifically, the ratio of the number of vehicles corresponding to each driving mode in each lane to the total number of vehicles in each lane is used as the statistical probability for each driving mode in each lane. This probability reflects the proportion of vehicles adopting a specific driving mode in that lane.

[0072] To illustrate this, consider this example: if 100 vehicles pass through a straight-ahead lane in one hour, and 70 of them are identified as going straight, then the statistical probability of going straight is 70 / 100 = 0.7, or 70%. Similarly, if 20 vehicles are identified as turning left in the same time period, then the statistical probability of turning left is 20 / 100 = 0.2, or 20%. This method allows us to obtain the statistical probabilities of different driving patterns in each lane. These probabilities are crucial for understanding traffic flow distribution, optimizing traffic signal control, designing more effective traffic management strategies, and improving road safety.

[0073] S104. Determine the intersection profile based on the driving modes of each lane at the target intersection and the statistical probabilities corresponding to the driving modes of each lane.

[0074] The intersection profile includes the driving patterns of each lane at the target intersection and the statistical probabilities corresponding to each driving pattern.

[0075] In this embodiment, by clustering the vehicle trajectories of each lane at the target intersection within a preset time period, typical driving patterns of different lanes can be identified. This analysis helps to refine the understanding of traffic flow distribution and behavior at the intersection. Statistical analysis of the number of vehicles in each lane and determination of the statistical probability corresponding to each lane's driving pattern reveals the frequency and probability of each driving pattern within different time periods, aiding in predicting the intersection's traffic conditions during specific time periods. Combining the driving patterns of each lane with their corresponding statistical probabilities, an intersection profile can be constructed. This profile provides autonomous vehicles with rich environmental information, including lane usage and traffic flow. This information dynamically reflects the intersection's traffic conditions, helping the autonomous driving system better adapt to complex traffic environments and improve the accuracy and safety of decision-making.

[0076] Figure 2 A schematic flowchart of an intersection portrait generation method provided in another embodiment of this application is shown.

[0077] S201 clusters the vehicle trajectories of each lane at the target intersection within a preset time period to determine the driving mode of each lane.

[0078] The description of S201 is the same as the description of S101 in the above embodiments, and will not be repeated here.

[0079] S202, count the number of vehicles in each lane.

[0080] The description of S202 is the same as the description of S102 in the above embodiments, and will not be repeated here.

[0081] S203 determines the statistical probability corresponding to the driving mode of each lane based on the number of vehicles in each lane.

[0082] The description of S203 is the same as the description of S103 in the above embodiments, and will not be repeated here.

[0083] S204. Determine the intersection profile based on the driving modes of each lane at the target intersection and the statistical probabilities corresponding to the driving modes of each lane.

[0084] The description of S204 is the same as the description of S104 in the above embodiments, and will not be repeated here.

[0085] S205, when the target vehicle is driving at the target intersection, the intersection image is sent to the terminal device associated with the target vehicle.

[0086] The target vehicle refers to a vehicle that will be traveling at or is currently traveling at the target intersection. For example, the target vehicle may be 100 meters, 200 meters, or 300 meters away from the target intersection. This can be set according to actual needs.

[0087] The terminal devices associated with the target vehicle refer to devices installed on or connected to the vehicle. These devices are capable of receiving, processing, and displaying various information from the intelligent transportation system (such as intersection images) to assist driving and improve road safety. Terminal devices associated with the target vehicle include, but are not limited to: in-vehicle infotainment systems, vehicle displays, smart rearview mirrors, intelligent transportation terminal hosts, mobile applications, and smartphones.

[0088] In this embodiment of the application, when the target vehicle is driving at the target intersection, the intersection image is sent to the terminal device associated with the target vehicle, so that the driver can obtain important traffic information in real time, make more reasonable driving decisions, and improve traffic efficiency and safety.

[0089] In an optional embodiment, S205, when the target vehicle is traveling at the target intersection, sends the intersection image to the terminal device associated with the target vehicle, specifically including:

[0090] When the target vehicle is traveling at the target intersection at the current time, the intersection profile for the preset time period prior to the current time is sent to the terminal device.

[0091] In this scenario, assuming the preset time period includes two time points, the current time is later than either of these two time points and is associated with the later of the two time points. The intersection profile is provided using data from the preset time period closest to the current time. For example, the preset time period is defined as a range of start and end times, such as from 8:00 AM to 9:00 AM. The current time is 9:01 AM. The preset time period can be from 8:00 AM to 9:00 AM. The intersection profile generated between 8:00 AM and 9:00 AM is sent to target vehicles traveling at the target intersection at the current time (9:01 AM). After receiving the intersection profile, the target vehicles will display this information to the drivers to help them understand the traffic conditions at the intersection.

[0092] For example, suppose a vehicle is about to pass through a busy intersection at 9:01 AM. The preset time for this intersection is from 8:00 AM to 9:00 AM. Traffic data from 8:00 AM to 9:00 AM is used to generate an intersection profile. During this period, the system collects the following data: 150 vehicles passed through the straight lane; 80 vehicles passed through the left-turn lane; and 50 vehicles passed through the right-turn lane. The system generates an intersection profile from this data, and when the vehicle is about to pass through the busy intersection at 9:01 AM, the system sends this information to the vehicle's in-vehicle navigation system at 9:01 AM. After seeing this information, the driver can understand that in the past hour, the straight lane was the busiest, followed by the left-turn lane, and the right-turn lane was relatively less busy. This service based on preset time-period data can help drivers better plan their routes, improve driving safety, and reduce congestion.

[0093] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] Corresponding to the intersection profile generation method described in the above embodiments, Figure 3 The diagram shows a structural block diagram of the intersection image generation device provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0095] Reference Figure 3 The intersection image generation device includes:

[0096] The driving mode determination module is used to cluster the vehicle trajectories of each lane at the target intersection within a preset time period to determine the driving mode of each lane.

[0097] The vehicle count module is used to count the number of vehicles in each lane.

[0098] The probability determination module is used to determine the statistical probability corresponding to the driving mode of each lane based on the number of vehicles in each lane.

[0099] The profile determination module is used to determine the profile of the intersection based on the driving patterns of each lane of the target intersection and the statistical probabilities corresponding to the driving patterns of each lane.

[0100] In one possible implementation, the intersection image generation device further includes:

[0101] The lane attribute acquisition module is used to acquire the lane attributes of each lane;

[0102] The driving mode determination module is used to cluster the vehicle trajectories of each lane based on the lane attributes of each lane to determine the driving mode of each lane.

[0103] In one possible implementation, the driving mode determination module is used for:

[0104] Based on the lane attributes of each lane, the vehicle trajectories of each lane are clustered to obtain the clustered trajectories of each lane.

[0105] The clustered trajectories are analyzed based on the lane attributes of each lane to identify and remove abnormal trajectories.

[0106] Based on the clustered trajectories after removing outliers, the driving mode of each lane is determined.

[0107] In one possible implementation, the number of vehicles in each lane includes the total number of vehicles in each lane and the number of vehicles corresponding to each driving mode in each lane; the probability determination module is used for:

[0108] The ratio of the number of vehicles corresponding to each driving mode in each lane to the total number of vehicles in each lane is used as the statistical probability of each driving mode in each lane.

[0109] In one possible implementation, the intersection image generation device further includes:

[0110] The sending module is used to send the intersection image to the terminal device associated with the target vehicle when the target vehicle is driving at the target intersection.

[0111] In one possible implementation, the sending module is used for:

[0112] When the target vehicle is traveling at the target intersection at the current time, the intersection profile for the preset time period prior to the current time is sent to the terminal device.

[0113] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0115] This application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0116] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0117] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0118] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 4 As shown, the computer device of this embodiment includes: at least one processor 20 ( Figure 4 (Only one is shown in the diagram), memory 21, and computer program 22 stored in the memory 21 and executable on the at least one processor 20, wherein the processor 20 executes the computer program 22 to implement the steps in any of the above-described intersection image generation method embodiments.

[0119] The computer device may include, but is not limited to, a processor 20 and a memory 21. Those skilled in the art will understand that... Figure 4The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0120] The processor 20 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0121] In some embodiments, the memory 21 may be an internal storage unit of the computer device, such as a hard disk or memory. In other embodiments, the memory 21 may be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, the memory 21 may include both internal and external storage units of the computer device. The memory 21 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0122] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / computer equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0123] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0124] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0128] The relevant user personal information that may be involved in the various embodiments of this application is processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the business scenario, and includes personal information that users actively provide or that is generated as a result of using the product / service, as well as personal information obtained with user authorization.

[0129] The personal information of users processed by the applicant will vary depending on the specific product / service scenario and will be based on the specific scenario in which the user uses the product / service. This may involve the user's account information, device information, driving information, vehicle information, or other related information. The applicant will treat the user's personal information and its processing with a high degree of diligence.

[0130] The applicant attaches great importance to the security of users' personal information and has taken reasonable and feasible security protection measures that comply with industry standards to protect users' information and prevent unauthorized access, disclosure, use, modification, damage or loss of personal information.

Claims

1. A method for generating intersection profiles, characterized in that, include: Cluster the vehicle trajectories of each lane at the target intersection within a preset time period to determine the driving mode of each lane. Count the number of vehicles in each lane; The statistical probability of the driving mode for each lane is determined based on the number of vehicles in each lane. Based on the driving patterns of each lane at the target intersection and the statistical probabilities corresponding to each driving pattern, an intersection profile is determined.

2. The intersection profiling generation method as described in claim 1, characterized in that, The method further includes: Obtain the lane attributes for each lane; The step of clustering vehicle trajectories in each lane of a target intersection within a preset time period to determine the driving mode of each lane includes: Based on the lane attributes of each lane, the vehicle trajectories of each lane are clustered to determine the driving mode of each lane.

3. The intersection profiling generation method as described in claim 2, characterized in that, The process of clustering vehicle trajectories in each lane based on lane attributes to determine the driving mode of each lane includes: Based on the lane attributes of each lane, the vehicle trajectories of each lane are clustered to obtain the clustered trajectories of each lane. The clustered trajectories are analyzed based on the lane attributes of each lane to identify and remove abnormal trajectories. Based on the clustered trajectories after removing outliers, the driving mode of each lane is determined.

4. The intersection profiling generation method according to any one of claims 1 to 3, characterized in that, The number of vehicles in each lane includes the total number of vehicles in each lane and the number of vehicles corresponding to each driving mode in each lane. The determination of the statistical probability corresponding to the driving mode of each lane based on the number of vehicles in each lane includes: The ratio of the number of vehicles corresponding to each driving mode in each lane to the total number of vehicles in each lane is used as the statistical probability of each driving mode in each lane.

5. The intersection profiling generation method according to any one of claims 1 to 3, characterized in that, The method further includes: When the target vehicle is driving at the target intersection, the intersection image is sent to the terminal device associated with the target vehicle.

6. The intersection profiling generation method as described in claim 5, characterized in that, The step of sending the intersection image to the terminal device associated with the target vehicle when the target vehicle is driving at the target intersection includes: When the target vehicle is traveling at the target intersection at the current time, the intersection profile for the preset time period prior to the current time is sent to the terminal device.

7. A crossroads image generation device, characterized in that, include: The driving mode determination module is used to cluster the vehicle trajectories of each lane at the target intersection within a preset time period to determine the driving mode of each lane. The statistics module is used to count the number of vehicles in each lane; The probability determination module is used to determine the statistical probability corresponding to the driving mode of each lane based on the number of vehicles in each lane. The intersection profile determination module is used to determine the intersection profile based on the driving patterns of each lane at the target intersection and the statistical probabilities corresponding to the driving patterns of each lane.

8. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, When the computer program product is run on a computer device, it causes the computer device to perform the method as described in any one of claims 1 to 6.

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