Roadside data display method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a roadside data display method and device, electronic equipment and a storage medium, and belongs to the technical field of traffic. The method comprises the following steps: writing data in a data text file of the vehicle-mounted equipment into a table file; a field message in a log file of the vehicle-mounted equipment is analyzed and converted into a JSON format, a target field is obtained, and the target field comprises a second timestamp, second basic safety information and second road side unit information; generating first lane data and first track data based on the first timestamp, the first basic safety information and the first roadside unit information, and generating second lane data and second track data based on the second timestamp, the second basic safety information and the second roadside unit information; and in response to the first instruction, displaying the first lane data, the first track data, the second lane data and the second track data on a map page. According to the embodiment of the invention, the roadside data can be fused and synchronously displayed.

Description

Technical Field

[0001] This application relates to the field of transportation technology, and in particular to a roadside data display method and apparatus, electronic device and storage medium. Background Technology

[0002] In real-world traffic scenarios, roadside equipment, as a core infrastructure of intelligent transportation systems and vehicle-road cooperation, is deployed along roads to achieve efficient information exchange between vehicles, roads, and the cloud. However, sometimes the streaming data from roadside equipment cannot be parsed in sync with traffic data, which may lead to information delays and outdated information.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to provide a roadside data display method, apparatus, electronic device, and storage medium, which aims to fuse and synchronously display roadside data.

[0005] To achieve the above objectives, one aspect of this application proposes a roadside data display method, the method comprising the following steps: The data in the data text file of the vehicle-mounted device is written into a table file, the table file including a first timestamp, first basic safety information and first roadside unit information; The field messages in the log file of the vehicle-mounted device are parsed and converted into JSON format to obtain the target field, which includes a second timestamp, second basic safety information and second roadside unit information; First lane data and first trajectory data are generated based on the first timestamp, the first basic safety information and the first roadside unit information; second lane data and second trajectory data are generated based on the second timestamp, the second basic safety information and the second roadside unit information; the first timestamp and the second timestamp correspond to each other. In response to the first instruction, the first lane data, the first trajectory data, the second lane data, and the second trajectory data are displayed on the map page.

[0006] In some embodiments, the method further comprises: The first basic safety information and the first roadside unit information are time-aligned and interpolated using the first timestamp; Calculate the first physical distance between the first basic safety information and the first roadside unit information after time alignment and interpolation; Longitude and latitude charts are generated using the first timestamp, the first basic security information, and the first roadside unit information; A distance chart is generated using the first timestamp and the first physical distance; In response to the second instruction, the longitude chart, the latitude chart, and the distance chart are displayed on the driving assistance page.

[0007] In some embodiments, generating first lane data and first trajectory data based on the first timestamp, the first basic safety information, and the first roadside unit information, and generating second lane data and second trajectory data based on the second timestamp, the second basic safety information, and the second roadside unit information, includes: The time range of the first basic safety information and the first roadside unit information is aligned using the first timestamp; The first basic safety information and the first roadside unit information are merged and aligned using the nearest neighbor matching merging principle to obtain the first merged information; First lane data and first trajectory data are generated based on the first merged information; The time range of the second basic safety information and the second roadside unit information is aligned using the second timestamp; The second basic safety information and the second roadside unit information are merged and aligned using the nearest neighbor matching merging principle to obtain the second merged information; Second lane data and second trajectory data are generated based on the second merged information; The first time stamp and the second time stamp are used to create a correspondence between the first lane data and the second lane data, and a correspondence between the first trajectory data and the second trajectory data is also created.

[0008] In some embodiments, the method further comprises: If data exists in the second basic security information, then the average latitude and average longitude of the second basic security information are determined as the center location of the map; If no data is found in the second basic security information, then map data is obtained, and the average latitude and average longitude of the map data are determined as the center position of the map. The map data includes longitude data and latitude data.

[0009] In some embodiments, displaying the first lane data, the first trajectory data, the second lane data, and the second trajectory data on a map page in response to a first instruction includes: In response to the first instruction, the first lane data, the first trajectory data, the second lane data, and the second trajectory data are displayed on the map page based on the center position of the map.

[0010] In some embodiments, after displaying the first lane data, the first trajectory data, the second lane data, and the second trajectory data on a map page in response to a first instruction, the method further includes: The first lane data, the first trajectory data, the second lane data, and the second trajectory data are input into a reinforcement learning model to obtain a traffic light timing scheme. The traffic light timing scheme is shared through V2X communication, and traffic lights at adjacent intersections are coordinated based on the traffic light timing scheme to form a green wave.

[0011] In some embodiments, after displaying the first lane data, the first trajectory data, the second lane data, and the second trajectory data on a map page in response to a first instruction, the method further includes: Acquire LiDAR point cloud data and camera image data; The Transformer architecture is used to extract features based on the first lane data, the first trajectory data, the second lane data, the second trajectory data, the LiDAR point cloud data, and the camera image data to obtain multi-source features; The association weights are calculated using a multi-head attention mechanism. The collision risk probability is obtained by using a Bayesian network based on the multi-source features and the associated weights.

[0012] In some embodiments, after displaying the first lane data, the first trajectory data, the second lane data, and the second trajectory data on a map page in response to a first instruction, the method further includes: Input the first lane data, the first trajectory data, the second lane data, and the second trajectory data into the LSTM traffic prediction model to obtain the future traffic trend; The SDN controller adjusts network parameters based on the future traffic trends.

[0013] To achieve the above objectives, another aspect of this application provides a roadside data display device, the device comprising: The first reading module is used to write data from the data text file of the vehicle-mounted device into a table file, the table file including a first timestamp, first basic safety information and first roadside unit information; The second reading module is used to parse the field messages in the log file of the vehicle-mounted device and convert them into JSON format to obtain the target field, which includes the second timestamp, the second basic safety information and the second roadside unit information. The trajectory generation module is used to generate first lane data and first trajectory data based on the first timestamp, the first basic safety information and the first roadside unit information, and to generate second lane data and second trajectory data based on the second timestamp, the second basic safety information and the second roadside unit information, wherein the first timestamp and the second timestamp correspond to each other; The display module is used to display the first lane data, the first trajectory data, the second lane data, and the second trajectory data on the map page in response to the first instruction.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a vehicle including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the aforementioned method.

[0017] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0018] The embodiments of this application include at least the following beneficial effects: This application provides a roadside data display method and device, electronic device and storage medium. This solution writes data from the data text file of the vehicle-mounted device into a table file; parses the field messages in the log file of the vehicle-mounted device and converts them into JSON format to obtain the target field; generates first lane data and first trajectory data based on a first timestamp, first basic safety information and first roadside unit information; and generates second lane data and second trajectory data based on a second timestamp, second basic safety information and second roadside unit information. This achieves real-time parsing and processing of traffic flow data (roadside data), which helps reduce data lag and data conflicts. In response to a first command, it displays the first lane data, first trajectory data, second lane data and second trajectory data on the map page. It can display roadside data, which helps improve the consistency of the fused data trajectory issued by the roadside, which is helpful for traffic management and decision-making. At the same time, it improves the user experience through the interactive map page. Attached Figure Description

[0019] Figure 1 This is a flowchart of the roadside data display method provided in the embodiments of this application; Figure 2 This is a flowchart of the chart generation steps in the roadside data display method provided in this application embodiment; Figure 3 yes Figure 1 The flowchart of step S103 in the process; Figure 4 This is a flowchart of the traffic light coordination steps in the roadside data display method provided in this application embodiment; Figure 5 The roadside data display method provided in this application embodiment also includes a flowchart of the vehicle decision-making steps; Figure 6 The roadside data display method provided in this application embodiment also includes a flowchart of a traffic prediction step; Figure 7 This is a flowchart of the dynamic network topology adjustment provided in the embodiments of this application; Figure 8 This is a specific implementation flow of the roadside data display method provided in this application embodiment when applied to an intelligent connected vehicle system; Figure 9 yes Figure 8 The flowchart of step S801 in the process; Figure 10 This is a schematic diagram of the processing of table files provided in the embodiments of this application; Figure 11 yes Figure 8 A flowchart of step S801 in the process; Figure 12 yes Figure 8 Another flowchart for step S803; Figure 13 This is a schematic diagram of the roadside data display device provided in the embodiments of this application; Figure 14 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0021] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0022] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0024] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0025] 1) The On-Board Unit (OBU) is a key device in intelligent transportation systems. It is usually installed inside the vehicle and is used to communicate with roadside units (RSUs), other vehicles and traffic management systems.

[0026] In real-world traffic scenarios, roadside equipment, as a core infrastructure of intelligent transportation systems and vehicle-road cooperation, is deployed along roads to achieve efficient information exchange between vehicles, roads, and the cloud. However, sometimes the streaming data from roadside equipment cannot be parsed in sync with traffic data, which may lead to information delays and outdated information.

[0027] In summary, the technical problems existing in the relevant technologies need to be improved.

[0028] In view of this, this application provides a roadside data display method, device, equipment, and medium. This solution writes data from a text file of an onboard device into a table file; parses field messages in the log file of the onboard device and converts them into JSON format to obtain target fields; generates first lane data and first trajectory data based on a first timestamp, first basic safety information, and first roadside unit information; and generates second lane data and second trajectory data based on a second timestamp, second basic safety information, and second roadside unit information. This achieves real-time parsing and processing of traffic flow data (roadside data), which helps reduce data lag and data conflicts. In response to a first command, it displays the first lane data, first trajectory data, second lane data, and second trajectory data on a map page. This ability to display roadside data helps improve the consistency of fused data trajectories issued by the roadside, aids in traffic management and decision-making, and improves the user experience through an interactive map page.

[0029] The roadside data display method provided in this application relates to the field of traffic technology. The roadside data display method provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the roadside data display method, but is not limited to the above forms.

[0030] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0031] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0032] Figure 1 This is an optional flowchart of the roadside data display method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0033] Step S101: Write the data from the data text file of the vehicle-mounted device into a table file.

[0034] Specifically, the table file includes a first timestamp, first basic safety information, and first roadside unit information.

[0035] In some embodiments, the contents of a data text file from an in-vehicle device are acquired, each line of data is parsed, and information in a specific format is extracted. The parsed data is saved to an Excel file, with each data item written to a specific cell in the Excel file.

[0036] During parsing, rows of data that cannot be parsed can be skipped, and these rows will be printed to the console.

[0037] Alternatively, data cleaning can be performed to ensure that the data in the file is free of duplicates, missing values, or outliers, thereby guaranteeing the accuracy of the data.

[0038] In some embodiments, the table file stores the first basic safety information in the bsm worksheet and the first roadside unit information in the rsm worksheet.

[0039] Furthermore, the system performs time alignment and interpolation on the first basic safety information and the first roadside unit information using the first timestamp; calculates the first physical distance between the first basic safety information and the first roadside unit information after time alignment and interpolation; generates a longitude chart and a latitude chart using the first timestamp, the first basic safety information, and the first roadside unit information; generates a distance chart using the first timestamp and the first physical distance; and displays the longitude chart, latitude chart, and distance chart on the driving assistance page in response to the second instruction.

[0040] In this embodiment, the data from the text file of the vehicle-mounted device is written into a table file, which prepares for subsequent analysis and trajectory generation.

[0041] Step S102: Parse the field messages in the log file of the vehicle-mounted device and convert them into JSON format to obtain the target field.

[0042] Specifically, the target fields include a second timestamp, second basic safety information, and second roadside unit information.

[0043] In some embodiments, the log file of the OBU device is read, the field messages therein are parsed and sent to other system components.

[0044] Optionally, regular expressions or text parsing techniques can be used to extract field information from the logs. Specifically, key fields such as timestamps, second basic security information, and second roadside unit information can be extracted from the OBU device's log files.

[0045] Furthermore, the second timestamp is parsed and the message interval is calculated; it is determined whether the message is RX or TX; hexadecimal data is extracted and processed; field messages of BSM and RSM are converted into different JSON formats according to the field message type; and messages are sent through the corresponding sockets.

[0046] Optionally, the second roadside unit information may include data from multiple roadside devices. In some embodiments, information such as timestamps, message types (RX / TX), and field types (BSM, RSM) can provide a structured, time-aligned representation of messages from different devices, which helps to accurately merge data across multiple devices.

[0047] In this embodiment, the field messages in the log file of the vehicle-mounted device are parsed and converted into JSON format to obtain the target fields, which prepares for subsequent analysis and trajectory generation.

[0048] Step S103: Generate first lane data and first trajectory data based on the first timestamp, first basic safety information and first roadside unit information; generate second lane data and second trajectory data based on the second timestamp, second basic safety information and second roadside unit information.

[0049] Specifically, the first timestamp corresponds to the second timestamp.

[0050] In some embodiments, the time ranges of the first basic safety information and the first roadside unit information are aligned by the first timestamp; the two datasets are merged by the principle of nearest neighbor matching and merging, and the first timestamp, the first basic safety information and the first roadside unit are combined to generate the corresponding first lane data and first trajectory data.

[0051] Specifically, the time ranges of the first basic safety information and the first roadside unit information are aligned using a first timestamp; the aligned first basic safety information and the first roadside unit information are merged using the nearest neighbor matching merging principle to obtain first merged information; first lane data and first trajectory data are generated based on the first merged information; the time ranges of the second basic safety information and the second roadside unit information are aligned using a second timestamp; the aligned second basic safety information and the second roadside unit information are merged using the nearest neighbor matching merging principle to obtain second merged information; second lane data and second trajectory data are generated based on the second merged information; a correspondence between the first lane data and the second lane data is created using the first timestamp and the second timestamp, and a correspondence between the first trajectory data and the second trajectory data is also created.

[0052] Furthermore, the absolute errors between speed and heading angle in the first and second merged information are calculated separately, and the absolute errors between speed and heading angle are displayed on the navigation interface.

[0053] In this embodiment, first lane data and first trajectory data are generated based on the first timestamp, first basic safety information and first roadside unit information, and second lane data and second trajectory data are generated based on the second timestamp, second basic safety information and second roadside unit information. This enables real-time parsing and processing of traffic flow data (roadside data), reduces data lag and data conflicts, and facilitates more accurate vehicle status monitoring and path analysis, as well as real-time vehicle monitoring and prediction.

[0054] Step S104: In response to the first instruction, display the first lane data, the first trajectory data, the second lane data, and the second trajectory data on the map page.

[0055] Specifically, the first instruction is triggered when the map page is opened, and is used to display the first lane data, the first trajectory data, the second lane data, and the second trajectory data.

[0056] In some embodiments, the center location of the map is determined by a second basic security information.

[0057] Optionally, if data exists in the second basic security information, the average latitude and average longitude of the second basic security information are determined as the center position of the map; if data does not exist in the second basic security information, map data is obtained, and the average latitude and average longitude of the map data are determined as the center position of the map, the map data including longitude data and latitude data.

[0058] Furthermore, the map center position of the second basic security information and the map center position of the first basic security information are calculated, and the midpoint between the two map center positions is determined as the final map center position.

[0059] Optionally, if data exists in the second basic security information, the maximum latitude, minimum latitude, maximum longitude, and minimum longitude of the second basic security information are determined as the map edge.

[0060] Furthermore, the maximum latitude, minimum latitude, maximum longitude, and minimum longitude of the second basic security information can be compared with the maximum latitude, minimum latitude, maximum longitude, and minimum longitude of the first basic security information, and the larger latitude, smaller latitude, larger longitude, and smaller longitude can be identified as map edges.

[0061] In some embodiments, the first lane data, the first trajectory data, the second lane data, and the second trajectory data are input into a reinforcement learning model to obtain a traffic light timing scheme; the traffic light timing scheme is shared through V2X communication, and traffic light coordination at adjacent intersections is achieved based on the traffic light timing scheme to form a green wave.

[0062] Optionally, LiDAR point cloud data and camera image data are acquired; a Transformer architecture is used to extract features based on the first lane data, first trajectory data, second lane data, second trajectory data, LiDAR point cloud data, and camera image data to obtain multi-source features; a multi-head attention mechanism is used to calculate the association weights; and a Bayesian network is used to obtain the collision risk probability based on the multi-source features and association weights.

[0063] It should be noted that the first lane data, first trajectory data, second lane data, and second trajectory data are input into the LSTM traffic prediction model to obtain the future traffic trend; the SDN controller adjusts the network parameters based on the future traffic trend.

[0064] In this embodiment, in response to the first instruction, the first lane data, the first trajectory data, the second lane data, and the second trajectory data are displayed on the map page based on the center position of the map. This can display roadside data, which helps to improve the consistency of the fused data trajectory issued by the roadside, and is helpful for traffic management and decision-making. At the same time, the interactive map page improves the user experience.

[0065] Steps S101 to S104 of this application embodiment involve writing data from the data text file of the vehicle-mounted device into a table file; parsing the field messages in the log file of the vehicle-mounted device and converting them into JSON format to obtain the target field; generating first lane data and first trajectory data based on the first timestamp, first basic safety information, and first roadside unit information; and generating second lane data and second trajectory data based on the second timestamp, second basic safety information, and second roadside unit information. This enables real-time parsing and processing of traffic flow data (roadside data), which helps reduce data lag and data conflicts. In response to the first instruction, the first lane data, first trajectory data, second lane data, and second trajectory data are displayed on the map page. This ability to display roadside data helps improve the consistency of the fused data trajectory issued by the roadside, which is beneficial for traffic management and decision-making. At the same time, the interactive map page improves the user experience.

[0066] See also Figure 2 In some embodiments, the roadside data display method provided in this application further includes a chart generation step, which may include, but is not limited to, steps S201 to S205: Step S201: Time alignment and interpolation of the first basic safety information and the first roadside unit information are performed using the first timestamp.

[0067] In step S201 of some embodiments, the first basic safety information and the first roadside unit information are time-aligned using a first timestamp, and missing values ​​of the first basic safety information and the first roadside unit information are supplemented using an interpolation method.

[0068] Understandably, data is time-aligned and interpolated to place them on the same timeline.

[0069] Step S202: Calculate the first physical distance between the first basic safety information after time alignment and interpolation and the first roadside unit information.

[0070] In step S202 of some embodiments, the first physical distance between the first basic safety information and the first roadside unit information is calculated using the Havesing formula (using geographic coordinates).

[0071] Furthermore, the processed data (including time, latitude, longitude, and distance) is saved to a TXT file.

[0072] Step S203: Generate longitude and latitude charts using the first timestamp, the first basic safety information, and the first roadside unit information.

[0073] In step S203 of some embodiments, a graph is drawn using the first timestamp as the horizontal axis and the longitude and latitude of the first basic safety information and the first roadside unit information as the vertical axis.

[0074] Optionally, the longitude and latitude charts include longitude and latitude data that change over time.

[0075] For example, in a longitude chart, the horizontal axis is the first timestamp and the vertical axis is the longitude of the vehicle, showing the longitude position over time.

[0076] Similarly, the horizontal axis represents the first timestamp, and the vertical axis represents the vehicle's latitude, showing the latitudinal position during the time change process.

[0077] Step S204: Generate a distance chart using the first timestamp and the first physical distance.

[0078] In step S204 of some embodiments, a distance chart is drawn using the first timestamp as the horizontal axis and the physical distance as the vertical axis.

[0079] Optionally, generate three charts (latitude, longitude, and distance) and save them as PNG files.

[0080] In step S205, in response to the second instruction, a longitude chart, a latitude chart, and a distance chart are displayed on the driving assistance page.

[0081] Specifically, the second command is triggered when the driver assistance page is opened, and is used to display longitude, latitude, and distance charts.

[0082] In step S205 of some embodiments, a longitude chart, a latitude chart, and a distance chart are displayed on the driving assistance page.

[0083] Optionally, the absolute error between speed and heading angle can be displayed on the driver assistance page.

[0084] See also Figure 3 In some embodiments, step S103 may include, but is not limited to, steps S301 to S307: Step S301: Align the time range of the first basic safety information with that of the first roadside unit information using the first timestamp.

[0085] In step S301 of some embodiments, the timestamps of the vehicle's basic safety information and the roadside unit information are aligned.

[0086] Step S302: The first basic safety information and the first roadside unit information after alignment are merged using the nearest neighbor matching merging principle to obtain the first merged information.

[0087] In step S302 of some embodiments, the first basic safety information and the first roadside unit information are merged by nearest neighbor matching. For each time stamp of basic safety information, the nearest roadside unit information is found and merged.

[0088] Optionally, the first merged information can be obtained by merging the aligned first basic safety information and the first roadside unit information through linear interpolation, spatial constraint association, and multi-target tracking.

[0089] Step S303: Generate first lane data and first trajectory data based on the first merging information.

[0090] In step S303 of some embodiments, first lane data and first trajectory data are generated based on the location information (such as longitude and latitude) and vehicle motion information (such as speed and heading angle) in the first merged information.

[0091] Optionally, lane recognition algorithms can be used to generate the first lane data. Specifically, the lane data includes information such as the vehicle's lane position, lane boundaries, and vehicle direction of travel.

[0092] In some embodiments, the vehicle's path can be obtained by tracking the vehicle's location over a period of time.

[0093] The smoothness of the trajectory can be improved by interpolation or curve fitting methods.

[0094] Step S304: Align the time range of the second basic safety information with that of the second roadside unit information using the second timestamp.

[0095] In step S304 of some embodiments, similar to step S301, the time range of the second basic safety information and the second roadside unit information is aligned by the second timestamp.

[0096] Step S305: The second basic safety information and the second roadside unit information after alignment are merged using the nearest neighbor matching merging principle to obtain the second merged information.

[0097] In step S305 of some embodiments, similar to step S302, the second basic safety information and the second roadside unit information after alignment are merged using the nearest neighbor matching merging principle to obtain the second merged information.

[0098] Step S306: Generate second lane data and second trajectory data based on the second merging information.

[0099] In step S306 of some embodiments, similar to step S303, second lane data and second trajectory data are generated based on the second merging information.

[0100] Step S307: Create a correspondence between the first lane data and the second lane data using the first timestamp and the second timestamp, and create a correspondence between the first trajectory data and the second trajectory data.

[0101] In step S307 of some embodiments, the first lane data corresponding to the first timestamp is matched with the second lane data corresponding to the second timestamp based on the timestamp. Nearest neighbor matching or interpolation methods can be used to ensure that data at two different time points can be correctly paired, thereby generating a correspondence between the first lane data and the second lane data.

[0102] It is understandable that the first trajectory data and the second trajectory data correspond to each other through the first timestamp and the second timestamp.

[0103] In this embodiment, a correspondence between the first lane data and the second lane data is created using the first timestamp and the second timestamp, and a correspondence between the first trajectory data and the second trajectory data is also created, which helps to improve the accuracy and consistency of the lane data and trajectory data.

[0104] See also Figure 4 In some embodiments, the roadside data display method provided in this application further includes a traffic light coordination step, which may include, but is not limited to, steps S401 to S402: Step S401: Input the first lane data, the first trajectory data, the second lane data, and the second trajectory data into the reinforcement learning model to obtain the traffic light timing scheme.

[0105] Optionally, real-time vehicle speed and steering intention are obtained from the on-board equipment, and the vehicle queue length is obtained based on the first lane data, the first trajectory data, the second lane data, and the second trajectory data.

[0106] In some embodiments, traffic signal cooperative optimization is achieved using reinforcement learning and multi-agent techniques. In the reinforcement learning model for a single intersection, the agent's state space S is determined by the vehicle queue length. Real-time vehicle speed , turning intention (i represents the lane number), first lane data, first trajectory data, second lane data, and second trajectory data, etc., constitute the action space A, which is the traffic light timing scheme. The reward function R optimizes the average vehicle delay time D and the queue length, and can be expressed as:

[0107] Where α and β are weighting coefficients, The length of the vehicle queue. For real-time vehicle speed, This indicates a change of direction.

[0108] Furthermore, during multi-agent collaborative optimization, V2X communication is used to share the status information of each intersection, and then the SDN controller adjusts the network parameters based on the status information of each intersection.

[0109] Step S402: Share the traffic light timing scheme through V2X communication, and realize the coordination of traffic lights at adjacent intersections based on the traffic light timing scheme to form a green wave.

[0110] In some embodiments, the green lights at adjacent intersections are coordinated by adjusting the traffic light cycles (the duration of green, yellow, and red lights).

[0111] It is understandable that the traffic light timing scheme is adjusted using the first lane data, the first trajectory data, the second lane data, and the second trajectory data.

[0112] Furthermore, SDN technology can be combined to achieve dynamic adjustment of network topology (such as bandwidth allocation and routing strategies).

[0113] It is understandable that vehicle-to-everything (V2X) technology can be used to share traffic data at various intersections, such as queue lengths and real-time vehicle speeds. Real-time data sharing allows for more precise adjustment of traffic light timings at adjacent intersections, resulting in more accurate green wave formation.

[0114] See also Figure 5 In some embodiments, the roadside data display method provided in this application further includes a vehicle decision-making step, which may include, but is not limited to, steps S501 to S504: Step S501: Acquire lidar point cloud data and camera image data.

[0115] Optionally, an extended Kalman filter (EKF) is used to fuse lidar and millimeter-wave radar data, with the state equation being:

[0116]

[0117] in, Let f be the state vector, and let f be the state transition function. For process noise, "where h is the measured value and h is the measurement function." For measuring noise.

[0118] State estimation is achieved through prediction and update steps:

[0119]

[0120] in, For the prediction and updated state estimation, Let covariance matrix be the variance matrix. For Kalman gain, It is a Jacobian matrix. Let be the noise covariance matrix.

[0121] It should be noted that particle filtering is used to process visual sensor data. The target position is estimated through importance sampling, weight update, and resampling steps. Multi-sensor time synchronization adopts the IEEE 1588v2 protocol to ensure that the time deviation Δt < 100μs.

[0122] Optionally, target recognition and classification are achieved based on camera image data. A target recognition model based on YOLOv7 or an improved Faster R-CNN is used, with data augmentation to expand the training samples and attention mechanism weights introduced into the loss function to improve target recognition accuracy. Target attribute classification employs SVM or MLP. Taking vehicle type classification as an example, the SVM decision function is:

[0123] in, For Lagrange multipliers, For sample labels, is the kernel function, and b is the bias term.

[0124] It should be noted that the collected data undergoes error calibration and correction. This correction can be applied to data collected by vehicle-mounted equipment, roadside equipment, and other devices, but is not limited to these.

[0125] An error model library was established, and regression analysis was used to construct the error model e=g(θ), where θ represents parameters such as environmental conditions and equipment status, and e represents the error value. Crowdsourced data fusion was performed by calculating the fusion result using a weighted average.

[0126] in, For the test data of each device, For the corresponding weights.

[0127] Furthermore, the multimodal fusion decision model based on V2X data adopts the Transformer architecture to realize multi-source information feature extraction and interaction.

[0128] Step S502: Using the Transformer architecture, feature extraction is performed based on the first lane data, the first trajectory data, the second lane data, the second trajectory data, the LiDAR point cloud data, and the camera image data to obtain multi-source features.

[0129] In some embodiments, roadside V2X data may include first lane data, first trajectory data, second lane data, and second trajectory data, and may also include other vehicle-to-everything (V2X) data.

[0130] Optionally, data fusion: jointly encoding roadside V2X data, LiDAR point clouds, and camera images into:

[0131] in, This is roadside V2X data. For lidar point clouds, Image from a camera.

[0132] Step S503: Calculate the association weights using a multi-head attention mechanism.

[0133] Furthermore, attention weights are calculated using a multi-head attention mechanism:

[0134] Where Q, K, and V are the query, key, and value matrices, respectively. The dimension of the key.

[0135] Furthermore, using Bayesian networks for uncertainty reasoning, the collision risk probability P(Collision|E) is calculated:

[0136] Among them, E represents evidence information such as the intentions of surrounding vehicles and the status of the vehicle itself provided by V2X data.

[0137] Step S504: Use a Bayesian network to obtain the collision risk probability based on multi-source features and associated weights.

[0138] It is understandable that collision risk probability is used to provide decision-making basis for autonomous vehicles.

[0139] See also Figure 6 In some embodiments, the roadside data display method provided in this application further includes a traffic prediction step, which may include, but is not limited to, steps S601 to S602: Step S601: Input the first lane data, first trajectory data, second lane data, and second trajectory data into the LSTM traffic prediction model to obtain the future traffic trend.

[0140] In some embodiments, the historical V2X data traffic sequence may include first lane data, first trajectory data, second lane data and second trajectory data, and may also include other historical vehicle-to-everything (V2X) data.

[0141] Optionally, an LSTM traffic prediction model is used, taking the historical V2X data traffic sequence Xt as input and updating the hidden state through LSTM units. and cell state The LSTM expression is as follows:

[0142]

[0143]

[0144]

[0145]

[0146] in, , , These are the input gate, forget gate, and output gate, respectively, and σ is the sigmoid function.

[0147] Step S602: Adjust network parameters based on future traffic trends using the SDN controller.

[0148] Furthermore, by combining SDN technology, dynamic network topology adjustment (such as bandwidth allocation and routing policies) can be achieved. The dynamic network topology adjustment process is as follows: Figure 7 As shown.

[0149] Taking intelligent connected vehicle systems as an example, Figure 8 This is a flowchart illustrating a specific implementation of the roadside data display method provided in this application when applied to an intelligent connected vehicle system. Figure 8 The methods may include, but are not limited to, the following steps: Step S801: Convert the TXT format bitstream data sent by the OBU into Excel, read the data text file containing field information, and write the parsed data into a table file according to a specific format.

[0150] Specifically, the data text files and table files include timestamps, hexadecimal data, etc. The data is parsed and processed, and the parsed data is written into an Excel file according to a specific format.

[0151] In some embodiments, a script is used to read a file and parse data. The script includes the following steps: 1) Input file processing: The script receives a .txt file from the command line.

[0152] 2) Data parsing: The script reads the contents of the input file, parses each line of data, and extracts information in a specific format.

[0153] 3) Excel file generation: The parsed data will be saved to an Excel file, and each data item will be written to a specific cell in the Excel file.

[0154] 4) Error handling: The script will skip unparseable data rows and print these data to the console.

[0155] Furthermore, the geographic data (such as lane information, trajectory data, etc.) in the Excel file is read out and visualized on an interactive map, ultimately generating an HTML file that can be viewed in a browser.

[0156] For example, the flowchart of S801 is as follows: Figure 9 As shown.

[0157] Optionally, the geographical location information of the two types of data (BSM and RSM) can be processed and analyzed using the Distance script, and the physical distance between them can be calculated. The Distance script workflow is as follows: Input: The script receives an Excel file path via command-line arguments. The file contains two worksheets (bsm and rsm), which store BSM and RSM data respectively.

[0158] deal with: Read BSM and RSM data.

[0159] Perform time alignment and interpolation on the data to ensure they are on the same timeline.

[0160] Calculate the physical distance between BSM and RSM targets (using geographic coordinates).

[0161] Plot a graph showing how latitude, longitude, and distance change over time.

[0162] Output the results to a text file.

[0163] Output: Generate three charts (latitude, longitude, and distance) and save them as PNG files.

[0164] Save the processed data (including time, latitude, longitude, and distance) to a TXT file.

[0165] For example, a schematic diagram of processing a table file is shown below. Figure 10As shown.

[0166] Step S802: Establish a communication server, read the log file of the OBU device, parse the field messages in it, and send them to other system components.

[0167] In some embodiments, the timestamp is parsed and the message interval is calculated; it is determined whether the message is RX or TX; hexadecimal data is extracted and processed; field messages of BSM and RSM are converted into different JSON formats according to the field message type; and the message is sent through the corresponding socket.

[0168] Specifically, the core methods for parsing a single log message include the following processing flow: 1. Parse the timestamp and calculate the message interval; 2. Determine whether to receive (RX) or send (TX) messages; 3. Extract and process hexadecimal data; 4. Convert messages to different JSON formats based on their type: BSM, RSM, and other TLV formats; 5. Send messages via the corresponding socket.

[0169] Optionally, write a Python script to process and parse V2X (vehicle-to-the-world) data. It decodes V2X messages and stores the parsed data in an Excel file. The following is a detailed explanation of the main functions and logic of this Python script: Import: openpyxl: Used to create and manipulate Excel files.

[0170] datetime and timedelta: used for time-related calculations.

[0171] binascii: Used for converting between binary data and ASCII.

[0172] asn1tools: Used to decode data in ASN.1 (Abstract Syntax Notation One) format.

[0173] re: Used for regular expression operations, such as parsing timestamps.

[0174] The ASN.1 decoder defines two ASN.1 decoders: sec_decoder: Used to decode messages defined in the SPDU.asn file.

[0175] day2_frame_decoder: Used to decode messages defined in the ASN Day 2.asn file. Header configuration: The header configuration of the Excel spreadsheet is defined to store different types of V2X message data. For example: The map header is used to store map-related data.

[0176] The BSM header is used to store Basic Safety Message data.

[0177] The spat header is used to store signal phase and timing data.

[0178] Create Excel Create a new Excel workbook and remove the default blank worksheet.

[0179] Create a worksheet for each V2X message type based on the header defined in SHEET_HEADERS, and set the header.

[0180] The data processing function includes the following steps: 1) Retrieve Nested Field Values: Used to extract values ​​from nested dictionary structures. Returns 'Field NA' if the field does not exist.

[0181] 2) Processing field values: Process values ​​according to field type: If it is byte data, convert it to a hexadecimal string.

[0182] If it is bit string data, convert it to a binary string.

[0183] Other data types are multiplied based on factors.

[0184] 3) Calculate GNSS time: Calculate the corresponding Unix time and ITS time based on the GNSS time.

[0185] 4) Save the data to Excel: 5) v2x message parsing function: This includes processing map information: parsing MAP messages and storing the data in an Excel map worksheet. It also involves processing node, connection, and lane information, and calculating relevant coordinates.

[0186] Processing BSM information: Processing RSI information Processing RSM information Step S803: Process the parsed RSM and BSM field data.

[0187] In some embodiments, a flowchart of step S803 is as follows: Figure 11 As shown in the diagram, the time ranges of the BSM and RSM data are aligned; the two datasets are merged using the nearest neighbor matching principle; the geographic distance between them is calculated using the geodesic method; the absolute error between speed and heading angle is calculated; and finally, the resulting image is plotted on the UI.

[0188] For example, another flowchart of step S803 is as follows: Figure 12 As shown.

[0189] Specifically, the methods for transmitting the parsed data to the UI in real time include: broadcasting the processed data via UDP, receiving packets by listening on a socket port to obtain field data in real time, decoding and parsing the ASN.1 data using the asn1c tool, and finally printing and displaying the intersection data and calculated deviation results in real time on the terminal.

[0190] It should be noted that the data parsing and processing methods include converting latitude and longitude (WGS-84) to ECEF (Geocentric-Earth-Fixed Coordinate System), and the conversion method is as follows:

[0191]

[0192] Where ϕ is the latitude, λ is the longitude, h is the altitude, and e is the Earth's eccentricity. Furthermore, the ECEF difference is converted into ENU, as follows:

[0193] Where ϕ is the latitude, λ is the longitude, h is the altitude, and e is the Earth's eccentricity. Optionally, the heading angle is encoded in units of 0.0125°. During decoding, this needs to be divided by 80. The decoding process is as follows:

[0194] The decoding process for message events in RSM is as follows:

[0195] The positioning error analysis of roadside field messages, including horizontal error and root mean square error, is as follows:

[0196] It should be noted that the development process of the visualization tool for real-time collected roadside data and processed deviation results is as follows: First, the map and lane data are initialized. Then, the data is processed by node, the vehicle trajectory data is grouped by vehicle ID, fixed marker points are set, the trajectory is drawn on the Gaode Map, and the file is saved as an HTML file.

[0197] The data in this application is transmitted to the UI low-latency development process in real time at a frequency of 50Hz, and the end-to-end latency between the road, cloud, and vehicle can reach 80ms. Furthermore, the use of optimized time synchronization and data pruning technology can ensure the uniqueness of the ID of the same target in the fused data.

[0198] In some embodiments, data field integrity checks can be performed. These checks can be performed on collected data, trajectory data, prediction data, and other data, but are not limited to these.

[0199] This includes establishing data format specifications and using regular expressions or XML Schema to validate data fields. For missing fields, linear interpolation is used to complete them, assuming that the missing data is known. The position x at time t is missing:

[0200] Simultaneously establish field association constraints, such as target velocity V>0, position coordinates:

[0201] Optionally, data continuity monitoring can be performed. This can include monitoring collected data, trajectory data, prediction data, and other types of data, but is not limited to these.

[0202] Establish a timestamp monitoring mechanism to calculate the time interval between adjacent data frames: ,when Data interruption is detected at certain times. A sliding window algorithm is used, with a window size of W and a data continuity rate CR of:

[0203] For interrupted data, prediction and completion are performed using an ARIMA model or a Seq2Seq model. The difference equation for the ARIMA model is as follows:

[0204] Where B is the shift operator, d is the difference order, and ϕ(B) For autoregressive polynomials and moving average polynomials, It is white noise.

[0205] In some embodiments, map data and BSM (Basic Safety Message) data in an Excel file are visualized as a map image.

[0206] Function: Calculates the center position of the map.

[0207] logic: If the BSM data is not empty, the average latitude and longitude of the BSM data are used as the center location.

[0208] If the BSM data is empty but the map data is not empty, the average latitude and longitude of the map data are used as the center location.

[0209] If both are empty, the default value [0, 0] is returned.

[0210] The detailed explanation of how lane data is drawn using functions is as follows: Function: Draw lane data.

[0211] logic: Iterate through each row in the map data (assuming each row represents a lane).

[0212] Extract the latitude and longitude data of the lanes and convert them to a numeric type.

[0213] If valid coordinate data for the lane is available, the lane trajectory is plotted and distinguished by different colors.

[0214] Mark the start (green) and end (red) of the lane.

[0215] Set the chart title, axis labels, legend, and grid.

[0216] The detailed explanation of plotting trajectory data using functions is as follows: Function: Draw trajectory data (such as BSM data).

[0217] logic: Extract the unique identifier of the trace based on trace_key_func.

[0218] Store the latitude and longitude data of the same trajectory in a dictionary.

[0219] Iterate through the dictionary, draw each trajectory, and distinguish them with different colors.

[0220] Mark the start (green) and end (red) of the trajectory.

[0221] Display the ID of the trajectory near the starting point.

[0222] Function: Draw BSM trajectory data.

[0223] logic: Call the plot_trace_data function, using id as the unique identifier for the trajectory.

[0224] The detailed explanation of generating map images through functions is as follows: Function: Visualizes map data and BSM data from an Excel file into a map image.

[0225] logic: Read map data (map sheet) and BSM data (bsm sheet) from an Excel file.

[0226] Calculate the center location of the map.

[0227] Create a plotting window to draw lane data and BSM trajectory data.

[0228] Disable the scientific notation display of the coordinate axes.

[0229] Set the image title, save it as a PNG file, and display the image.

[0230] Optionally, scene coverage assessment is performed. This involves dividing the road into M*N grid cells, with each grid cell having a detection probability... Calculated based on the detection range of the V2X device and the sensor's field of view. K random scenes are generated through Monte Carlo simulation, with scene coverage C as follows:

[0231] in, This represents the number of times grid (i, j) was detected. The actual coverage is assessed by comparing data from on-site scanning using drones or vehicle-mounted equipment.

[0232] This application embodiment writes data from the data text file of the vehicle-mounted device into a table file; parses the field messages in the log file of the vehicle-mounted device and converts them into JSON format to obtain the target fields; generates first lane data and first trajectory data based on the first timestamp, first basic safety information, and first roadside unit information; and generates second lane data and second trajectory data based on the second timestamp, second basic safety information, and second roadside unit information. This achieves real-time parsing and processing of traffic flow data (roadside data), which helps reduce data lag and data conflicts. In response to the first command, the first lane data, first trajectory data, second lane data, and second trajectory data are displayed on the map page. This ability to display roadside data helps improve the consistency of the fused data trajectory issued by the roadside, which is helpful for traffic management and decision-making. At the same time, the interactive map page improves the user experience.

[0233] See also Figure 13 This application also provides a roadside data display device that can implement the above-described roadside data display method. The device includes: The first reading module 1301 is used to write data from the data text file of the vehicle-mounted device into a table file, the table file including a first timestamp, first basic safety information and first roadside unit information; The second reading module 1302 is used to parse the field messages in the log file of the vehicle-mounted device and convert them into JSON format to obtain the target field, which includes the second timestamp, the second basic safety information and the second roadside unit information. The trajectory generation module 1303 is used to generate first lane data and first trajectory data based on the first timestamp, the first basic safety information and the first roadside unit information, and to generate second lane data and second trajectory data based on the second timestamp, the second basic safety information and the second roadside unit information, wherein the first timestamp and the second timestamp correspond to each other; Display module 1304 is used to display the first lane data, the first trajectory data, the second lane data and the second trajectory data on a map page in response to the first instruction.

[0234] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0235] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned roadside data display method. This electronic device can be any smart terminal, including a tablet computer or an in-vehicle computer.

[0236] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0237] See also Figure 14 , Figure 14 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 1401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1402 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1402 and is called and executed by the processor 1401 to execute the roadside data display method of the embodiments of this application. The input / output interface 1403 is used to implement information input and output; The communication interface 1404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1405 transmits information between various components of the device (e.g., processor 1401, memory 1402, input / output interface 1403, and communication interface 1404); The processor 1401, memory 1402, input / output interface 1403 and communication interface 1404 are connected to each other within the device via bus 1405.

[0238] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described roadside data display method.

[0239] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0240] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0241] This application also provides a vehicle, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program implements the above-described method when executed by the processor.

[0242] It is understood that the content of the above method embodiments is applicable to this vehicle embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0243] This application also provides a computer program product, including a computer program that implements the above-described method when executed by a processor.

[0244] It is understood that the content of the above method embodiments is applicable to the computer program product embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0245] The roadside data display method, roadside data display device, electronic device, and storage medium provided in this application embodiment write data from the data text file of the vehicle-mounted device into a table file; parse the field messages in the log file of the vehicle-mounted device and convert them into JSON format to obtain the target fields; generate first lane data and first trajectory data based on a first timestamp, first basic safety information, and first roadside unit information; and generate second lane data and second trajectory data based on a second timestamp, second basic safety information, and second roadside unit information. This achieves real-time parsing and processing of traffic flow data (roadside data), which helps reduce data lag and data conflicts. In response to a first command, the first lane data, first trajectory data, second lane data, and second trajectory data are displayed on a map page. This ability to display roadside data helps improve the consistency of the fused data trajectory issued by the roadside, which is helpful for traffic management and decision-making. At the same time, the interactive map page improves the user experience.

[0246] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0247] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0248] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0249] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0250] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0251] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0252] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0253] The units described above 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.

[0254] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0255] 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, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0256] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A roadside data display method, characterized in that, The method includes the following steps: The data in the data text file of the vehicle-mounted device is written into a table file, the table file including a first timestamp, first basic safety information and first roadside unit information; The field messages in the log file of the vehicle-mounted device are parsed and converted into JSON format to obtain the target field, which includes a second timestamp, second basic safety information and second roadside unit information; First lane data and first trajectory data are generated based on the first timestamp, the first basic safety information and the first roadside unit information; second lane data and second trajectory data are generated based on the second timestamp, the second basic safety information and the second roadside unit information; the first timestamp and the second timestamp correspond to each other. In response to the first instruction, the first lane data, the first trajectory data, the second lane data, and the second trajectory data are displayed on the map page.

2. The method according to claim 1, characterized in that, The method further includes: The first basic safety information and the first roadside unit information are time-aligned and interpolated using the first timestamp; Calculate the first physical distance between the first basic safety information and the first roadside unit information after time alignment and interpolation; Longitude and latitude charts are generated using the first timestamp, the first basic security information, and the first roadside unit information; A distance chart is generated using the first timestamp and the first physical distance; In response to the second instruction, the longitude chart, the latitude chart, and the distance chart are displayed on the driving assistance page.

3. The method according to claim 1, characterized in that, The process of generating first lane data and first trajectory data based on the first timestamp, the first basic safety information, and the first roadside unit information, and generating second lane data and second trajectory data based on the second timestamp, the second basic safety information, and the second roadside unit information, includes: The time range of the first basic safety information and the first roadside unit information is aligned using the first timestamp; The first basic safety information and the first roadside unit information are merged and aligned using the nearest neighbor matching merging principle to obtain the first merged information; First lane data and first trajectory data are generated based on the first merged information; The time range of the second basic safety information and the second roadside unit information is aligned using the second timestamp; The second basic safety information and the second roadside unit information are merged and aligned using the nearest neighbor matching merging principle to obtain the second merged information; Second lane data and second trajectory data are generated based on the second merged information; The first time stamp and the second time stamp are used to create a correspondence between the first lane data and the second lane data, and a correspondence between the first trajectory data and the second trajectory data is also created.

4. The method according to claim 1, characterized in that, After displaying the first lane data, the first trajectory data, the second lane data, and the second trajectory data on the map page in response to the first instruction, the method further includes: The first lane data, the first trajectory data, the second lane data, and the second trajectory data are input into a reinforcement learning model to obtain a traffic light timing scheme. The traffic light timing scheme is shared through V2X communication, and traffic lights at adjacent intersections are coordinated based on the traffic light timing scheme to form a green wave.

5. The method according to claim 1, characterized in that, After displaying the first lane data, the first trajectory data, the second lane data, and the second trajectory data on the map page in response to the first instruction, the method further includes: Acquire LiDAR point cloud data and camera image data; The Transformer architecture is used to extract features based on the first lane data, the first trajectory data, the second lane data, the second trajectory data, the LiDAR point cloud data, and the camera image data to obtain multi-source features; The association weights are calculated using a multi-head attention mechanism. The collision risk probability is obtained by using a Bayesian network based on the multi-source features and the associated weights.

6. The method according to claim 1, characterized in that, After displaying the first lane data, the first trajectory data, the second lane data, and the second trajectory data on the map page in response to the first instruction, the method further includes: Input the first lane data, the first trajectory data, the second lane data, and the second trajectory data into the LSTM traffic prediction model to obtain the future traffic trend; The SDN controller adjusts network parameters based on the future traffic trends.

7. A roadside data display device, characterized in that, The device includes: The first reading module is used to write data from the data text file of the vehicle-mounted device into a table file, the table file including a first timestamp, first basic safety information and first roadside unit information; The second reading module is used to parse the field messages in the log file of the vehicle-mounted device and convert them into JSON format to obtain the target field, which includes the second timestamp, the second basic safety information and the second roadside unit information. The trajectory generation module is used to generate first lane data and first trajectory data based on the first timestamp, the first basic safety information and the first roadside unit information, and to generate second lane data and second trajectory data based on the second timestamp, the second basic safety information and the second roadside unit information, wherein the first timestamp and the second timestamp correspond to each other; The display module is used to display the first lane data, the first trajectory data, the second lane data, and the second trajectory data on the map page in response to the first instruction.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method of 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 of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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