Method, device and medium for detecting passage state of blocked section

Abstract

The present disclosure provides a method and device for detecting the traffic state of a blocked road section, and a related equipment and medium, which belong to the technical field of data processing, and particularly relate to the fields of navigation and intelligent transportation. The implementation scheme is as follows: obtaining a plurality of vehicle trajectories in a preset time range; clustering the plurality of vehicle trajectories to obtain at least one center trajectory; for each road section in the blocked road section and a first road section, performing the following statistical operations: determining the lane category corresponding to each vehicle trajectory passing through each road section based on the matching relationship between the plurality of vehicle trajectories and a road network; and counting the number of trajectories corresponding to each lane category in each road section; and determining the traffic state of the blocked road section based on the distance between each center trajectory and the center line of each road section and the number of trajectories corresponding to each lane category in each road section.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to navigation technology and intelligent transportation, specifically to a method, device, electronic device, computer-readable storage medium, and computer program product for detecting the traffic status of a blocked road section. Background Technology

[0002] 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.

[0003] In the real world, when a road is impassable due to traffic control, road construction, or other reasons, navigation maps mark that section as impassable (i.e., blocked) and avoid it during navigation route planning. The road needs to be reopened promptly once traffic control and road construction are completed.

[0004] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for detecting the passage status of a blocked road section.

[0006] According to one aspect of this disclosure, a method for detecting the traffic status of a blocked road segment is provided. The method includes: acquiring multiple vehicle driving trajectories within a preset time range, the multiple vehicle driving trajectories including trajectories passing through a preset range of the blocked road segment, the blocked road segment and a first road segment forming a set of parallel roads, and the first road segment being in a passable state; clustering the multiple vehicle driving trajectories to obtain at least one central trajectory; for each road segment in the blocked road segment and the first road segment, performing the following statistical operations: determining the lane category corresponding to each vehicle driving trajectory passing through each road segment based on the matching relationship between the vehicle driving trajectory and the road network, the lane category being related to the exit direction of the corresponding vehicle driving trajectory after passing through each road segment; counting the number of trajectories corresponding to each lane category in each road segment; and determining the traffic status of the blocked road segment based on the distance between each central trajectory and the centerline of each road segment and the number of trajectories corresponding to each lane category in each road segment, the traffic status indicating whether the blocked road segment can be opened.

[0007] According to another aspect of this disclosure, a traffic status detection device for a blocked road segment is provided. The device includes: a first acquisition unit configured to acquire multiple vehicle travel trajectories within a preset time range, the multiple vehicle travel trajectories including trajectories passing through a preset range of the blocked road segment, the blocked road segment and a first road segment forming a set of parallel roads, and the first road segment being in a passable state; a second acquisition unit configured to cluster the multiple vehicle travel trajectories to obtain at least one central trajectory; and a statistics unit configured to perform a statistical operation for each road segment in the blocked road segment and the first road segment, the statistics unit including: a first determination subunit configured to determine the lane category corresponding to each vehicle travel trajectory passing through each road segment based on the matching relationship between the vehicle travel trajectory and the road network, the lane category being related to the exit direction of the corresponding vehicle travel trajectory after passing through each road segment; and a first statistics subunit configured to count the number of trajectories corresponding to each lane category in each road segment; and a judgment unit configured to judge the traffic status of the blocked road segment based on the distance between each central trajectory and the centerline of each road segment and the number of trajectories corresponding to each lane category in each road segment, the traffic status indicating whether the blocked road segment can be opened.

[0008] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned method for detecting the passage status of a blocked road segment.

[0009] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the above-described method for detecting the passage status of a blocked road segment.

[0010] According to another aspect of this disclosure, a computer program product is provided, including a computer program, wherein the computer program, when executed by a processor, implements the above-described method for detecting the passage status of a blocked road segment.

[0011] 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

[0012] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A schematic diagram of an exemplary system in which the various methods described herein may be implemented according to embodiments of the present disclosure is shown;

[0014] Figure 2 A flowchart of a method for detecting the traffic status of a blocked road section according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A flowchart illustrating the clustering of vehicle driving trajectories according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A schematic diagram of lane-level trajectory clustering according to an exemplary embodiment of the present disclosure is shown;

[0017] Figure 5 A flowchart illustrating the acquisition of driving speed information according to an embodiment of the present disclosure is shown;

[0018] Figure 6 A schematic diagram of a method for detecting the traffic status of a blocked road section according to an exemplary embodiment of the present disclosure;

[0019] Figure 7 A structural block diagram of a traffic status detection device for a blocked road section according to an embodiment of the present disclosure is shown;

[0020] Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0021] 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 of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0023] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0024] In the real world, when roads are impassable due to traffic control, road construction, or other reasons, navigation maps mark the road segment as impassable (i.e., blocked) and avoid it during navigation route planning. When traffic control or road construction ends, the road needs to be reopened promptly. In parallel road scenarios such as main and auxiliary roads, or sections of elevated highways, due to insufficient accuracy of user devices and trajectory drift, one or more drifting trajectories may cause roads that are still impassable to be mistakenly opened, preventing users from planning routes and impacting the navigation experience.

[0025] According to one or more embodiments of this disclosure, a method for detecting the traffic status of blocked road sections is provided. By extracting the distance comparison relationship between the center trajectory and the center line of each road section and the relationship relationship between the number of trajectories of each lane between parallel roads in the parallel road scenario, the traffic status of the blocked road section is comprehensively analyzed and judged based on the above-mentioned correlation information. In this way, the accuracy of detecting the traffic status of blocked roads can be improved through the comprehensive analysis of multi-dimensional information.

[0026] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0027] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatus described herein can be implemented according to embodiments of this disclosure is shown. Reference Figure 1 The system 100 includes one or more client devices 101, 102, 103, 104, 105 and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105 and 106 can be configured to execute one or more applications.

[0028] In embodiments of this disclosure, server 120 may run one or more services or software applications that enable the execution of the above-described method for detecting the traffic status of blocked road sections.

[0029] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, such as to users of client devices 101, 102, 103, 104, 105, and / or 106 under a Software as a Service (SaaS) model.

[0030] exist Figure 1 In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 can sequentially interact with server 120 using one or more client applications to utilize the services provided by these components. It should be understood that various different system configurations are possible and may differ from system 100. Therefore, Figure 1 This is an example of a system used to implement the various methods described herein, and is not intended to be limiting.

[0031] Users can use client devices 101, 102, 103, 104, 105, and / or 106 to obtain vehicle driving trajectories. The client devices can provide an interface that allows users to interact with them. The client devices can also output information to the user through this interface. Although... Figure 1 Only six client devices are described, but those skilled in the art will understand that this disclosure can support any number of client devices.

[0032] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors, or other sensing devices. These computer devices can run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as Google Chrome OS); or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablets, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing various applications, such as various internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and can use various communication protocols.

[0033] Network 110 can be any type of network well known to those skilled in the art, and can use any of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.) to support data communication. By way of example only, one or more networks 110 can be a local area network (LAN), an Ethernet-based network, a token ring network, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0034] Server 120 may include one or more general-purpose computers, special-purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0035] The computing unit in server 120 can run one or more operating systems, including any of the aforementioned operating systems and any commercially available server operating system. Server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0036] In some implementations, server 120 may include one or more applications to analyze and merge data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105 and / or 106. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105 and / or 106.

[0037] In some implementations, server 120 can be a server for a distributed system or a server integrated with blockchain. Server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in the cloud computing service system, designed to address the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0038] System 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. Databases 130 may reside in various locations. For example, a database used by server 120 may be local to server 120, or it may be located away from server 120 and may communicate with server 120 via a network-based or dedicated connection. Databases 130 may be of different types. In some embodiments, the database used by server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data from and from the databases in response to commands.

[0039] In some embodiments, one or more of the databases 130 may also be used by an application to store application data. The databases used by the application may be of different types, such as key-value stores, object stores, or regular stores supported by a file system.

[0040] Figure 1The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatus described in this disclosure.

[0041] According to embodiments of this disclosure, such as Figure 2 As shown, a method for detecting the traffic status of a blocked road segment is provided. The method includes: step S201, acquiring multiple vehicle driving trajectories within a preset time range, the multiple vehicle driving trajectories including trajectories passing through the blocked road segment within a preset range, the blocked road segment and a first road segment forming a set of parallel roads, and the first road segment being in a passable state; step S202, clustering the multiple vehicle driving trajectories to obtain at least one central trajectory; step S203, for each road segment in the blocked road segment and the first road segment, performing the following statistical operations: step S2031, based on the matching relationship between vehicle driving trajectories and the road network, determining the lane category corresponding to each vehicle driving trajectory passing through each road segment, the lane category being related to the exit direction of the corresponding vehicle driving trajectory after passing through each road segment; and step S2032, counting the number of trajectories corresponding to each lane category in each road segment; and step S204, based on the distance between each central trajectory and the centerline of each road segment and the number of trajectories corresponding to each lane category in each road segment, determining the traffic status of the blocked road segment, the traffic status indicating whether the blocked road segment can be opened.

[0042] Therefore, by extracting the distance comparison relationship between the center trajectory and the center line of each road segment in the parallel road scenario, as well as the relationship between the number of trajectories of each lane between parallel roads, and then comprehensively analyzing and judging the traffic status of the blocked road segment based on the above-mentioned correlation information, the accuracy of detecting the traffic status of blocked roads can be improved through the comprehensive analysis of multi-dimensional information.

[0043] In some embodiments, the preset range of the blocked road section can be a range within a preset distance from the centerline of the blocked road section (e.g., within 50m to the left and right of the blocked road section).

[0044] In some embodiments, the blocked road segment and the first road segment constitute a set of parallel roads. For example, the blocked road segment and the first road segment can be two relatively parallel road segments, such as main and auxiliary roads or sections on and under an elevated bridge.

[0045] In some embodiments, the preset time range may be, for example, the most recent day or the most recent 12 hours, and there is no limitation on this.

[0046] In some embodiments, clustering multiple vehicle trajectories to obtain at least one central trajectory can employ either holistic trajectory clustering or segmented trajectory clustering. Holistic trajectory clustering treats a trajectory as a whole without segmentation, clustering them by defining a similarity function, ensuring that each trajectory belongs to only one cluster. Segmented trajectory clustering divides a trajectory into multiple segments, where the sum of the segments can be the original trajectory or extracted features from the original trajectory. Subsequent clustering allows the same trajectory to potentially belong to multiple clusters, resulting in a visual effect of both traffic splitting and clustering.

[0047] In some embodiments, the blocked section includes at least one lane category, and the number of at least one center track is the same as the number of lane categories.

[0048] Therefore, by clustering vehicle trajectories and comparing the distances between each central trajectory and the center lines of two road segments, the accuracy of detection can be further improved.

[0049] In one example, a blocked road segment may include left-turn lanes, straight-ahead lanes, and right-turn lanes, resulting in 3 lane categories for each segment. Accordingly, the number of cluster center trajectories obtained from clustering can be set to 3. In another example, a blocked road segment may include straight-ahead lanes and right-turn lanes, resulting in 2 lane categories for each segment. Accordingly, the number of cluster center parameters can be set to 2.

[0050] In some embodiments, such as Figure 3 As shown, clustering multiple vehicle trajectories to obtain at least one central trajectory may include: step S301, establishing multiple road perpendiculars for the blocked road segment to determine the intersection point of each vehicle trajectory with each road perpendicular; step S302, clustering the multiple intersection points on each road perpendicular to obtain at least one trajectory center point corresponding to each road perpendicular, the number of trajectory center points being the same as the number of lane categories; and step S303, obtaining at least one central trajectory based on at least one trajectory center point corresponding to each of the multiple road perpendiculars.

[0051] Therefore, by establishing road perpendicular lines, road network data is integrated with real vehicle trajectory data, and the location data of the intersection of vehicle trajectory and road perpendicular lines are used to determine the road center point, thereby improving the accuracy of each center trajectory.

[0052] Figure 4 A schematic diagram of lane-level trajectory clustering according to an exemplary embodiment of the present disclosure is shown.

[0053] In some exemplary embodiments, such as Figure 4As shown, the line segment with an arrow labeled 401 represents a blocked road section; the line labeled 402 represents several vehicle trajectories corresponding to this blocked road section. First, multiple road perpendicular lines 403 can be established for the blocked road section 401. In one example, a series of road perpendicular lines can be established at equal intervals to divide the blocked road section. After establishing the road perpendicular lines, the position of the intersection point 404 between each vehicle trajectory and the blocked road section can be determined.

[0054] Subsequently, clustering can be performed based on the location data of multiple intersection points 404 along the same road perpendicular to determine at least one trajectory center point along that road perpendicular. The number of cluster centers can be the same as the number of lane categories.

[0055] In some embodiments, any clustering method such as GMM, K-Means, or DBSCAN can be used to obtain the trajectory cluster center point corresponding to each lane category, without any restrictions.

[0056] After obtaining at least one trajectory center point of each road perpendicular line, the center trajectory corresponding to each lane category on the blocked road can be obtained by connecting them.

[0057] In some embodiments, the matching relationship between trajectory data and the road network can be obtained based on multiple vehicle driving trajectories within a preset time range using a matching algorithm (e.g., a Hidden Markov Model-based map matching algorithm). Based on the trajectory matching results, all original trajectories for the blocking road segment and the first road segment matched to the parallel road are obtained. For example, if a trajectory sequentially traverses the road and its connected left-turn road, the trajectory is recorded as one left-turn crossing of that road. Similarly, the straight-through volume and right-turn volume of the road can be obtained, i.e., the number of trajectories corresponding to each lane category in the aforementioned road segment.

[0058] In some embodiments, the traffic status of a blocked road segment can be determined based on the distance between each center trajectory and the centerline of each road segment, as well as the number of trajectories corresponding to each lane category in each road segment. This can be achieved by determining whether each data point meets preset conditions. For example, for a blocked road segment with left-turn lanes, right-turn lanes, and straight lanes, if more than half of the center trajectories are closer to the blocked road segment, and within a preset time range, the number of trajectories for one or more lanes in the blocked road segment increases by more than a preset threshold, while the number of trajectories for the corresponding lanes in the first road segment decreases or remains essentially unchanged, then the blocked road segment can be determined to be passable, and its traffic status on the map can be updated.

[0059] For example, although more than half of the center trajectories are closer to the blocked section, if the number of trajectories in one or more lanes of the blocked section and the first section increases at the same time, it is judged that this situation may be due to the increased travel volume during holidays, resulting in more trajectory drift and thus greater trajectory interference. In this case, it is judged that the status of the blocked section does not need to be changed.

[0060] In some embodiments, the distance between the aforementioned center trajectory and the centerline of each road segment can be calculated using any method such as Euclidean distance or Dynamic Time Warping (DTW) distance, without any limitation.

[0061] In some embodiments, the above data can also be input into a pre-trained neural network model to compare the distance between the center trajectory and the center line of each road segment and to analyze the changing relationship between the number of trajectories of each lane category between parallel roads, thereby outputting the traffic status prediction results of the blocked road segment.

[0062] In some embodiments, the above statistical operation may further include: for each lane category of each road segment in the blocked road segment and the first road segment, obtaining the driving speed information of the vehicle driving trajectory corresponding to each lane category.

[0063] Furthermore, the determination of the traffic status of a blocked road segment based on the distance between each central trajectory and the centerline of each road segment, as well as the number of trajectories corresponding to each lane category in each road segment, includes: determining the traffic status of a blocked road segment based on the distance between each central trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and driving speed information.

[0064] In some cases, when roads are closed due to construction or repairs, the speeds of these obstructing vehicles and other vehicles driving illegally differ significantly from those of normal drivers. Therefore, the speeds of vehicles on different road sections can be used as reference information in the analysis and detection of road closures, thereby improving accuracy.

[0065] In some embodiments, the average driving speed of each vehicle's trajectory on the corresponding road segment can be obtained first, and then the driving speed of each lane category in each road segment can be statistically analyzed.

[0066] In some embodiments, the median of the average driving speed of the trajectory corresponding to each lane category can be determined as the driving speed information for each lane category. Alternatively, the average of the average driving speed of the trajectory corresponding to each lane category can be determined as the driving speed information for each lane category.

[0067] In some embodiments, the average driving speed of each trajectory corresponding to each lane category can be directly used as the driving speed information corresponding to each lane category.

[0068] In some embodiments, based on the aforementioned preset conditions, further preset conditions related to driving speed information can be determined to comprehensively assess the traffic status of blocked road segments. For example, if the median driving speed corresponding to one or more lane categories on a blocked road segment is less than a preset threshold, or if the difference between the median driving speed and the median driving speed of the corresponding lane on the first road segment is greater than a preset threshold, then each blocked road segment is determined to still be in a blocked state.

[0069] In some embodiments, the above data, along with driving speed information, can be input into a pre-trained neural network model to analyze the distance between the center trajectory and the center line of each road segment, the relationship between the number of trajectories of each lane category between parallel roads, and the relationship between the driving speed on parallel roads, thereby outputting the traffic status prediction results of the blocked road segment.

[0070] In some embodiments, such as Figure 5 As shown, for each lane category in each road segment and the first road segment, obtaining the driving speed information of the vehicle trajectory corresponding to each lane category can include:

[0071] Step S501: For each road segment in the blocked road segment and the first road segment, divide each road segment into multiple sub-road segments;

[0072] Step S502: For each lane category of each sub-segment of each road segment, based on the average driving speed of each vehicle's trajectory corresponding to each lane category on each sub-segment, determine the sub-segment driving speed information corresponding to each lane category on each sub-segment; and

[0073] Step S503: For each lane category on each road segment, determine the driving speed information corresponding to each lane category based on the driving speed information of multiple sub-road segments corresponding to each lane category.

[0074] In some situations, when vehicles are driving normally on the road, their speeds change significantly when entering, exiting, and entering the road. Therefore, to further characterize these changes, the entire road segment can be divided into multiple sub-segments, for example, into three segments (e.g., setting the segmentation criteria as: the first 10m before entering the road, the last 10m before exiting the road, and the remaining middle section of the road, resulting in the road entry segment, exit segment, and middle segment). Then, for the vehicle trajectories of different lane categories, the driving speed information of each sub-segment of each lane category on each road segment can be obtained (e.g., the median of the average speed of the driving trajectory on each sub-segment).

[0075] In some embodiments, the driving speed information of the above-mentioned sub-road segments can be used as feature data, and together with data such as the distance between the center trajectory and the center line of each road segment, and the number of trajectories of each lane category between parallel roads, it can be input into a pre-trained neural network model for comprehensive analysis to obtain the traffic status prediction results of the blocked road segments output by the model.

[0076] Therefore, by further dividing the road segment into different sections and statistically analyzing the speed information of each sub-segment, it is possible to obtain the speed information of vehicles at different positions on the road segment. This information on the speed changes of vehicles on the road segment can be incorporated into the analysis and detection of the aforementioned road blockage status, thereby improving the accuracy of road blockage status detection.

[0077] In some embodiments, determining the traffic status of a blocked road segment based on the distance between each central trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information may include: determining the trajectory features corresponding to the blocked road segment based on the distance between each central trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information; and determining the traffic status of the blocked road segment based on the trajectory features and road network static features, wherein the road network static features include at least one of the following: lane category information contained in the blocked road segment, lane category information contained in the first road segment, and the distance between the blocked road segment and the first road segment.

[0078] Therefore, by further incorporating the static characteristics of the road network into the analysis and detection of the aforementioned road blockage status, the accuracy of road blockage status detection can be improved.

[0079] In some embodiments, road network static features may include at least one of the lane category information contained in the blocked road segment, the lane category information contained in the first road segment, and the distance between the blocked road segment and the first road segment.

[0080] In some embodiments, the lane category information included in the blocked road segment may include whether the blocked road segment has a left-turn lane, a straight-ahead lane, or a right-turn lane. In some exemplary embodiments, the presence of a lane can be recorded as 1, and the absence of a lane can be recorded as 0.

[0081] In some embodiments, the lane category information included in the first road segment may include whether the first road segment has a left-turn lane, a straight-ahead lane, or a right-turn lane. In some exemplary embodiments, the presence of a lane can be recorded as 1, and the absence of a lane can be recorded as 0.

[0082] In some embodiments, the distance between the blocked road segment and the first road segment can be calculated using any method such as Euclidean distance or Dynamic Time Warping (DTW) distance, without any limitation.

[0083] In some embodiments, the data related to the vehicle's driving trajectory can be organized into a trajectory feature vector, and the static features of the road network can be organized into a static feature vector. Both can be input into a trained neural network model, and a comprehensive analysis can be performed based on the model to obtain the final traffic status detection result.

[0084] In some embodiments, the preset time range may include multiple time periods.

[0085] Based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information, determining the trajectory characteristics corresponding to the blocked road segment may include: based on the distance between each center trajectory and each road segment in each time period, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information, determining multiple trajectory characteristics of the blocked road segment corresponding to multiple time periods, wherein the multiple time periods include the first time period closest to the current time and at least one other second time period.

[0086] Furthermore, determining the traffic status of a blocked road segment based on trajectory features and road network static features may include: obtaining the predicted trajectory features of a first time period based on at least one second trajectory feature corresponding to at least one second time period from multiple trajectory features; and determining the traffic status of the blocked road segment based on the first trajectory feature corresponding to the first time period, the predicted trajectory feature, and the road network static features from multiple trajectory features.

[0087] Therefore, by utilizing the trend and periodicity of the trajectory characteristics changes of each road segment, the trajectory characteristics that the blocked road segment should have in the first time period can be predicted. By combining the static characteristics of the road network, the relationship between the predicted trajectory characteristics and the actual trajectory characteristics (i.e., the first trajectory characteristics) can be analyzed, thereby predicting the current traffic status and improving the accuracy of detecting the traffic status of blocked roads.

[0088] In some embodiments, a preset time range can be divided into multiple time periods. For example, if the preset time range is 24 hours, it can be divided by hours, with each hour serving as a time period.

[0089] Subsequently, the data related to vehicle driving trajectories in the aforementioned multiple time periods can be statistically analyzed to obtain the trajectory features corresponding to each time period. Among them, the multiple trajectory features can be divided into the first trajectory feature of the last time period (i.e., the first time period closest to the current time period) and the second trajectory features corresponding to the remaining time periods (second time periods). First, one or more of the above-mentioned second trajectory features are input into a pre-trained neural network model to predict the trajectory features of the first time period.

[0090] In some embodiments, when there are multiple second trajectory features, these multiple second trajectory features can further reflect the temporal characteristics of the vehicle's driving trajectory data. Neural network models can be applied to further analyze and mine the temporal features in the feature sequence formed by the multiple second trajectory features, thereby obtaining more accurate prediction results.

[0091] In some embodiments, the above-described temporal neural network model may be applied, such as the ARIMA model, LSTM model, GRU model, and Prophet model, etc., without limitation.

[0092] Furthermore, the predicted trajectory features obtained above, the actual first trajectory features corresponding to the first time period, and the static features of the road network can be further extracted and fused. The differences between the predicted trajectory features and the first trajectory features can be comprehensively analyzed (if the difference is large, the probability that the blocked road section is passable is higher), and the passability status of the blocked road section can be determined based on the difference.

[0093] Figure 6 A schematic diagram of a method for detecting the traffic status of a blocked road section according to an exemplary embodiment of the present disclosure is shown.

[0094] In some exemplary embodiments, the trajectory feature vector within each time period may include the following information:

[0095] 1) The distance of the center trajectory (left turn lane, right turn lane, straight lane) corresponding to each lane category from the centerline of the blocked section and the first section. If a lane category does not exist, it is recorded as the default value 0. Therefore, this feature information has a total of 3*2=6 dimensions. This feature information can be used to mine the center trajectory offset distance features of parallel roads before and after road closure. Multiple center trajectories corresponding to each lane category can more accurately represent the trajectory offset distance.

[0096] 2) The number of trajectories for each lane category (left-turn lane, right-turn lane, and straight-ahead lane) on the blocked section and the first road segment. If a lane category does not exist, it is recorded as the default value of 0. Therefore, this feature information has a total of 3*2=6 dimensions. This feature information is used to explore the correlation of traffic flow changes on parallel roads before and after road closure. For example, if the traffic flow on the blocked section and the first road segment increases simultaneously, it may be due to trajectory interference caused by increased travel volume during holidays; however, if the traffic flow on the blocked section increases significantly, while the traffic flow on its parallel road segments decreases slightly or remains the same, it is easier to deduce that the blocked section has been reopened.

[0097] 3) Speed ​​information for each sub-segment within each lane category (left-turn lane, right-turn lane, straight lane) of the blocked section and the first section. If a lane category does not exist, it is recorded as the default value 0. Therefore, this feature information has a total of 3*3*2=18 dimensions. This feature information is used to mine the correlation of speed changes on parallel roads before and after road closure. For example, when the vehicle speed on the blocked section is significantly lower than that on its parallel sections, these trajectories may be the trajectories of construction vehicles or vehicles violating regulations; when the vehicle speed on the blocked section increases significantly and approaches that of its parallel sections, it is easier to deduce that the blocked section has been reopened.

[0098] Therefore, the trajectory feature vector for the t-th time period is x t It is a feature vector with dimensions (1, 30).

[0099] The static characteristics of a road network may include the following information:

[0100] 1) Lane category information of the blocked road section: Whether there is a left turn lane, a straight lane, or a right turn lane in the blocked road section. If there is, it can be recorded as 1, and if there is no, it can be recorded as 0.

[0101] 2) Lane category information included in the first road segment: Whether there are left-turn lanes, straight lanes, and right-turn lanes in the first road segment. If they exist, they can be recorded as 1; if they do not exist, they can be recorded as 0.

[0102] 3) The distance between the centerline of the blocked section and the centerline of the first section.

[0103] Therefore, the static feature of the road network is a feature vector with dimension (1, 7).

[0104] like Figure 6 As shown, the traffic status detection model for blocked road sections consists of two main parts. The first part mainly uses a Long Short-Term Memory (LSTM) neural network to extract the trend and periodicity of lane-level trajectory changes. That is, it extracts the trajectory feature vectors x1, x2, ..., xt from multiple time periods before the t-th time period. t-1 The data is fed into a trained long short-term memory neural network to obtain x at time t. tPredicted value (i.e., the trajectory characteristics of time period t when the road is not open).

[0105] The second part involves combining static road network features with at least one feature extraction layer to mine the difference between the predicted trajectory feature value and the actual trajectory feature value at time t (that is, the aforementioned static road network features, x t Predicted value and x t The true value is input into the feature extraction layer to obtain the extracted features, and then the output layer is used to analyze the above features and output the final traffic state prediction result.

[0106] In some embodiments, the feature extraction layer and the output layer described above may be applied with fully connected layers, and there are no restrictions on this.

[0107] In some embodiments, for the above-mentioned road blockage traffic status detection model, historical trajectory data and the corresponding road blockage and traffic status information can be used to construct corresponding sample data and input into the model accordingly. The model is trained by constructing a loss function using predicted values ​​and true values.

[0108] In some embodiments, such as Figure 7 As shown, a traffic status detection device 700 for a blocked road section is provided, comprising: a first acquisition unit 710, configured to acquire multiple vehicle travel trajectories within a preset time range, the multiple vehicle travel trajectories including trajectories passing through a preset range of the blocked road section, the blocked road section and a first road section forming a set of parallel roads, and the first road section being in a passable state; a second acquisition unit 720, configured to cluster the multiple vehicle travel trajectories to obtain at least one central trajectory; and a statistics unit 730, configured to perform a statistical operation for each road section in the blocked road section and the first road section, the statistics unit 730 comprising: a first... A determining subunit 731 is configured to determine the lane category corresponding to each vehicle's travel trajectory passing through each road segment based on the matching relationship between the vehicle's travel trajectory and the road network. The lane category is related to the exit direction of the corresponding vehicle's travel trajectory after passing through each road segment. A first statistical subunit 732 is configured to count the number of trajectories corresponding to each lane category in each road segment. A judging unit 740 is configured to judge the traffic status of the blocked road segment based on the distance between each center trajectory and the centerline of each road segment and the number of trajectories corresponding to each lane category in each road segment. The traffic status indicates whether the blocked road segment can be opened.

[0109] The operations performed by units 710-740, subunit 731 and subunit 732 in the road blockage status detection device 700 are similar to the operations of steps S201-S204, S2031 and S2032 in the road blockage status detection method described above, and will not be described in detail here.

[0110] In some embodiments, the blocked section includes at least one lane category, and the number of at least one center track is the same as the number of lane categories.

[0111] In some embodiments, the second acquisition unit may include: a second determining subunit configured to establish multiple road perpendiculars for the blocked road segment to determine the intersection point of each vehicle's travel trajectory with each road perpendicular; a first acquisition subunit configured to cluster the multiple intersection points on each road perpendicular to obtain at least one trajectory center point corresponding to each road perpendicular, the number of trajectory center points being the same as the number of lane categories; and a second acquisition subunit configured to obtain at least one center trajectory based on at least one trajectory center point corresponding to each of the multiple road perpendiculars.

[0112] In some embodiments, the statistics unit may further include: a third acquisition subunit, configured to acquire driving speed information of vehicle driving trajectory corresponding to each lane category for each lane category of each road segment in the blocked road segment and the first road segment; and wherein the judgment unit may further be configured to: determine the traffic status of the blocked road segment based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment and the driving speed information.

[0113] In some embodiments, the third acquisition subunit may also be configured to: divide each road segment into multiple sub-segments for each road segment in the blocked road segment and the first road segment; determine the sub-segment driving speed information corresponding to each lane category on the sub-segment based on the average driving speed of each vehicle driving trajectory corresponding to each lane category on the sub-segment for each lane category on each road segment; and determine the driving speed information corresponding to each lane category based on the driving speed information of multiple sub-segments corresponding to each lane category on each road segment.

[0114] In some embodiments, the determining unit may include: a third determining subunit configured to determine the trajectory features corresponding to the blocked road segment based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and driving speed information; and a determining subunit configured to determine the traffic status of the blocked road segment based on the trajectory features and road network static features, wherein the road network static features include at least one of the following: lane category information contained in the blocked road segment, lane category information contained in the first road segment, and the distance between the blocked road segment and the first road segment.

[0115] In some embodiments, the preset time range includes multiple time periods, and the third determining subunit can be further configured to: determine multiple trajectory features of the blocked road segment corresponding to multiple time periods based on the distance between each center trajectory and each road segment in each time period, the number of trajectories corresponding to each lane category in each road segment, and driving speed information, wherein the multiple time periods include a first time period closest to the current time and at least one other second time period; and wherein the judging subunit can be further configured to: obtain the predicted trajectory features of the first time period based on at least one second trajectory feature corresponding to at least one second time period among the multiple trajectory features; and judge the traffic status of the blocked road segment based on the first trajectory feature corresponding to the first time period, the predicted trajectory feature, and the road network static features among the multiple trajectory features.

[0116] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0117] According to embodiments of this disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0118] refer to Figure 8 The present invention describes a structural block diagram of an electronic device 800 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can 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.

[0119] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0120] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, output unit 807, storage unit 808, and communication unit 809. Input unit 806 can be any type of device capable of inputting information to electronic device 800. Input unit 806 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device, and can include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 807 can be any type of device capable of presenting information, and can include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 808 can include, but is not limited to, disk and optical disk. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and can include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0121] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 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 801 performs the various methods and processes described above, such as the above-described method for detecting the passage status of a blocked road section. For example, in some embodiments, the above-described method for detecting the passage status of a blocked road section can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the above-described method for detecting the passage status of a blocked road section can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the above-described method for detecting the passage status of blocked road sections by any other suitable means (e.g., by means of firmware).

[0122] 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.

[0123] 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.

[0124] 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. A machine-readable medium 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0129] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A method for detecting the traffic status of a blocked road section, the method comprising: The driving trajectories of multiple vehicles within a preset time range are obtained. The driving trajectories of multiple vehicles include trajectories that pass through the preset range of the blocked road section. The blocked road section and the first road section form a set of parallel roads, and the first road section is in a passable state. Cluster the multiple vehicle trajectories to obtain at least one central trajectory; For each of the blocked road segment and the first road segment, perform the following statistical operations: Based on the matching relationship between the multiple vehicle driving trajectories and the road network, the lane category corresponding to each vehicle driving trajectory passing through each road segment is determined, and the lane category is related to the exit direction of the corresponding vehicle driving trajectory after passing through each road segment; as well as Count the number of trajectories corresponding to each lane category in each road segment; as well as Based on the distance between each center trajectory and the centerline of each road segment, and the number of trajectories corresponding to each lane category in each road segment, the traffic status of the blocked road segment is determined, and the traffic status indicates whether the blocked road segment can be opened.

2. The method according to claim 1, wherein, The blocked section includes at least one lane category, and the number of the at least one center track is the same as the number of lane categories.

3. The method according to claim 2, wherein, The step of clustering the multiple vehicle trajectories to obtain at least one central trajectory includes: Multiple road perpendicular lines are established for the blocked road section to determine the intersection point of each vehicle's trajectory with each road perpendicular line; Clustering is performed on multiple intersection points along each road perpendicular to obtain at least one trajectory center point corresponding to each road perpendicular, wherein the number of trajectory center points is the same as the number of lane categories; and The at least one center trajectory is obtained based on at least one trajectory center point corresponding to each of the multiple road perpendicular lines.

4. The method according to any one of claims 1 to 3, wherein, The statistical operations also include: For the blocked road segment and for each lane category within each segment of the first road segment, obtain the vehicle speed information corresponding to each lane category; and wherein, The determination of the traffic status of the blocked road segment based on the distance between each center trajectory and the centerline of each road segment, and the number of trajectories corresponding to each lane category in each road segment, includes: Based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information, the traffic status of the blocked road segment is determined.

5. The method according to claim 4, wherein, The step of obtaining the driving speed information of the vehicle driving trajectory corresponding to each lane category for each lane category in the blocked road segment and each road segment in the first road segment includes: For each of the blocked road sections and the first road section, each road section is divided into multiple sub-road sections; For each lane category of each sub-segment of each road segment, based on the average speed of each vehicle's trajectory corresponding to each lane category on each sub-segment, the driving speed information for each lane category of each sub-segment is determined; and For each lane category on each road segment, the driving speed information corresponding to each lane category is determined based on the driving speed information of multiple sub-road segments corresponding to each lane category.

6. The method according to claim 4 or 5, wherein, The determination of the traffic status of the blocked road segment based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information includes: Based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information, the trajectory characteristics corresponding to the blocked road segment are determined; and Based on the trajectory features and road network static features, the traffic status of the blocked road segment is determined. The road network static features include at least one of the following: lane category information contained in the blocked road segment, lane category information contained in the first road segment, and the distance between the blocked road segment and the first road segment.

7. The method according to claim 6, wherein, The preset time range includes multiple time periods. The determination of the trajectory characteristics corresponding to the blocked road segment based on the distance between each central trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and driving speed information includes: Based on the distance between each center trajectory and each road segment in each time period, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information, the blocked road segment is determined to correspond to multiple trajectory features in the multiple time periods, wherein the multiple time periods include a first time period closest to the current time and at least one other second time period; and wherein, The determination of the traffic status of the blocked road segment based on the trajectory features and road network static features includes: Based on at least one second trajectory feature corresponding to at least one second time period from the plurality of trajectory features, the predicted trajectory features of the first time period are obtained; and Based on the first trajectory feature corresponding to the first time period among the multiple trajectory features, the predicted trajectory feature, and the road network static features, the traffic status of the blocked road section is determined.

8. A device for detecting the traffic status of a blocked road section, the device comprising: The first acquisition unit is configured to acquire multiple vehicle driving trajectories within a preset time range. The multiple vehicle driving trajectories include trajectories passing through the preset range of the blocked road section. The blocked road section and the first road section constitute a set of parallel roads, and the first road section is in a passable state. The second acquisition unit is configured to cluster the driving trajectories of the plurality of vehicles to obtain at least one central trajectory. A statistical unit is configured to perform statistical operations for each of the blocked road segment and the first road segment, the statistical unit comprising: The first determining subunit is configured to determine the lane category corresponding to each vehicle's trajectory passing through each road segment based on the matching relationship between the vehicle's trajectory and the road network, wherein the lane category is related to the exit direction of the corresponding vehicle's trajectory after passing through each road segment; and The first statistical subunit is configured to count the number of trajectories corresponding to each lane category in each road segment; and The judgment unit is configured to determine the traffic status of the blocked road segment based on the distance between each center trajectory and the centerline of each road segment and the number of trajectories corresponding to each lane category in each road segment. The traffic status indicates whether the blocked road segment can be opened.

9. The apparatus according to claim 8, wherein, The blocked section includes at least one lane category, and the number of the at least one center track is the same as the number of lane categories.

10. The apparatus according to claim 9, wherein, The second acquisition unit includes: The second determining subunit is configured to establish multiple road perpendicular lines for the blocked road section to determine the intersection point of each vehicle's trajectory with each road perpendicular line; The first acquisition subunit is configured to cluster multiple intersections along each road perpendicular to obtain at least one trajectory center point corresponding to each road perpendicular, wherein the number of trajectory center points is the same as the number of lane categories; and The second acquisition subunit is configured to obtain the at least one center trajectory based on at least one trajectory center point corresponding to each of the multiple road perpendiculars.

11. The apparatus according to any one of claims 8 to 10, wherein, The statistical unit also includes: The third acquisition subunit is configured to acquire, for each lane category of the blocked road segment and each lane category of each road segment in the first road segment, the driving speed information of the vehicle driving trajectory corresponding to each lane category; and wherein, The judgment unit is further configured to: determine the traffic status of the blocked road segment based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information.

12. The apparatus according to claim 11, wherein, The third acquisition subunit is further configured to: For each of the blocked road sections and the first road section, each road section is divided into multiple sub-road sections; For each lane category of each road segment and each sub-segment, based on the average driving speed of each vehicle's trajectory corresponding to each lane category on each sub-segment, the driving speed information of each lane category corresponding to each sub-segment is determined. as well as For each lane category on each road segment, the driving speed information corresponding to each lane category is determined based on the driving speed information of multiple sub-road segments corresponding to each lane category.

13. The apparatus according to claim 11 or 12, wherein, The judgment unit includes: The third determining subunit is configured to determine the trajectory features corresponding to the blocked road segment based on the distance between each center trajectory and the centerline of each road segment, the number of trajectories corresponding to each lane category in each road segment, and driving speed information; and The judgment subunit is configured to determine the traffic status of the blocked road segment based on the trajectory features and road network static features. The road network static features include at least one of the following: lane category information contained in the blocked road segment, lane category information contained in the first road segment, and the distance between the blocked road segment and the first road segment.

14. The apparatus according to claim 13, wherein, The preset time range includes multiple time periods, and the third determining subunit is further configured to: Based on the distance between each center trajectory and each road segment in each time period, the number of trajectories corresponding to each lane category in each road segment, and the driving speed information, the blocked road segment is determined to correspond to multiple trajectory features in the multiple time periods, wherein the multiple time periods include a first time period closest to the current time and at least one other second time period; and wherein, The judgment subunit is further configured to: Based on at least one second trajectory feature corresponding to at least one second time period from the plurality of trajectory features, the predicted trajectory features of the first time period are obtained; and Based on the first trajectory feature corresponding to the first time period among the multiple trajectory features, the predicted trajectory feature, and the road network static features, the traffic status of the blocked road section is determined.

15. An electronic device comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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

Images