Roadside data quality evaluation method, electronic equipment, storage medium and program product

Abstract

The invention provides a roadside data quality evaluation method, electronic equipment, a storage medium and a program product, and the method comprises the steps: carrying out the preprocessing of collected roadside data, and obtaining the roadside data with a normal track; grouping the roadside data according to the unique identification numbers of the traffic targets; based on the grouped roadside data, quality evaluation indexes related to the driving state or dynamic parameters of the traffic target object are calculated, and the quality evaluation indexes comprise at least one of a course hopping rate, a speed hopping rate, a type hopping rate, a frequency hopping rate and a position hopping rate; and generating a data quality report based on the quality evaluation index, and performing quality evaluation on the roadside data according to the data quality report. Through multi-dimensional quantitative evaluation, the quality of the acquired roadside data can be comprehensively and accurately evaluated. The reliability of the data used in downstream data analysis and model training is ensured, and a solid data basis is provided for the research of intelligent traffic and automatic driving.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to a roadside data quality evaluation method, electronic device, storage medium, and program product. Background Technology

[0002] With the rapid development of technology, intelligent transportation systems have been widely applied in our daily lives. These systems extensively use roadside sensing devices to collect massive amounts of traffic data, which plays a crucial role in autonomous driving, real-time traffic monitoring, and dynamic road condition analysis.

[0003] However, in practical applications, the collected data is frequently affected by various external factors, such as signal interference, equipment malfunctions, and errors during data transmission. These factors can cause jumps in the collected data regarding location, heading, speed, type, or frequency. These jumps severely affect the accuracy and reliability of the data, impacting its reliability in subsequent applications. Current technologies lack comprehensive and systematic methods for evaluating the quality of roadside data, making it impossible to effectively quantify these quality issues.

[0004] Therefore, it is necessary to propose a roadside data quality evaluation method to comprehensively and systematically assess the quality of roadside data. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides a roadside data quality evaluation method, electronic device, storage medium, and program product, which provides multi-dimensional quality index evaluation to enhance the application value of roadside data.

[0006] To achieve the above objectives and other advantages, this application adopts the following technical solution:

[0007] Firstly, this application provides a method for evaluating the quality of roadside data, including:

[0008] The collected roadside data is preprocessed to obtain roadside data with normal trajectories;

[0009] The roadside data is grouped according to the unique identifier of the traffic target;

[0010] Based on the grouped roadside data, a quality evaluation index related to the driving state or dynamic parameters of the traffic target is calculated. The quality evaluation index includes at least one of the following: heading jump rate, speed jump rate, type jump rate, frequency jump rate, and position jump rate.

[0011] Based on the quality evaluation indicators, a data quality report is generated, and the roadside data is assessed for quality according to the data quality report.

[0012] According to the roadside data quality evaluation method provided in this application, the step of preprocessing the collected roadside data to obtain roadside data with normal trajectories includes:

[0013] The roadside data is sorted by time.

[0014] According to preset filtering conditions, noise data in the roadside data is filtered out. The filtering conditions include filtering trajectories whose duration is within a set time period.

[0015] According to the roadside data quality evaluation method provided in this application, when calculating the heading hop rate, the step of calculating the quality evaluation index related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data includes:

[0016] For the trajectories formed by different types of traffic targets, calculate the change in heading angle between adjacent frames of data in the roadside data;

[0017] If the change in heading angle exceeds the preset heading angle change threshold, it is determined to be a heading angle jump;

[0018] For the roadside data collected by the same roadside data acquisition device, the heading jump rate is calculated based on the number of trajectories with heading angle jumps and the total number of trajectories.

[0019] According to the roadside data quality evaluation method provided in this application, when calculating the speed jump rate, the step of calculating the quality evaluation index related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data includes:

[0020] For the trajectories formed by different types of traffic targets, calculate the velocity change between adjacent frames of data in the roadside data;

[0021] If the change in heading angle exceeds the preset speed change threshold, it is determined to be a speed jump;

[0022] For the roadside data collected by the same roadside data acquisition device, the speed jump rate is calculated based on the number of trajectories with speed jumps and the total number of trajectories.

[0023] According to the roadside data quality evaluation method provided in this application, when calculating the type jump rate, the step of calculating the quality evaluation index related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data includes:

[0024] Determine whether the type of the unique identifier between adjacent frames in each group of roadside data has changed. If it has changed, it is determined to be a type jump.

[0025] For the roadside data collected by the same roadside data acquisition device, the type change rate is calculated based on the number of trajectories that have undergone type changes and the total number of trajectories.

[0026] According to the roadside data quality evaluation method provided in this application, when calculating the frequency jump rate, the step of calculating the quality evaluation index related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data includes:

[0027] Calculate the data acquisition frequency for each group of roadside data;

[0028] If the data acquisition frequency is not within the preset frequency threshold range, it is determined to be a frequency jump;

[0029] For the roadside data collected by the same roadside acquisition device, the frequency jump rate is calculated based on the number of trajectories with frequency jumps and the total number of trajectories.

[0030] According to the roadside data quality evaluation method provided in this application, when calculating the location jump rate, the step of calculating the quality evaluation index related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data includes:

[0031] Calculate the distance and time difference between adjacent frames in each group of roadside data;

[0032] Based on the distance and time difference, calculate the positional change between adjacent frames and convert it into velocity;

[0033] For trajectories formed by different types of traffic targets, if the position change exceeds the preset position change threshold, it is judged as a position jump;

[0034] For the roadside data collected by the same roadside data acquisition device, the position jump rate is calculated based on the number of trajectories with position jumps and the total number of trajectories.

[0035] Secondly, this application provides an electronic device, the electronic device comprising:

[0036] One or more processors; and a memory storing computer program instructions that, when executed, cause the processors to perform the roadside data quality assessment method as described above.

[0037] Thirdly, this application provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the roadside data quality evaluation method as described above.

[0038] Fourthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the roadside data quality evaluation method as described above.

[0039] This application provides a roadside data quality evaluation method, electronic device, storage medium, and program product. The method involves preprocessing collected roadside data to obtain roadside data with normal trajectories; grouping the roadside data according to the unique identifier of traffic targets; calculating quality evaluation indicators related to the driving state or dynamic parameters of traffic targets based on the grouped roadside data; and including at least one of the following: heading jump rate, speed jump rate, type jump rate, frequency jump rate, and position jump rate. A data quality report is generated based on the quality evaluation indicators, and the roadside data quality is assessed according to the data quality report. This application provides in-depth analysis and processing of roadside data, and through multi-dimensional quantitative evaluation, it can comprehensively and accurately assess the quality of the collected roadside data. Quality assessment can filter out high-quality data, enhancing the application value of roadside data, ensuring the reliability of data in downstream data analysis and model training, and providing a solid data foundation for research on intelligent transportation and autonomous driving. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other implementation methods can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is one of the flowcharts illustrating the roadside data quality evaluation method provided in the embodiments of this application;

[0042] Figure 2 This is a second schematic flowchart of the roadside data quality evaluation method provided in the embodiments of this application;

[0043] Figure 3 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0044] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0045] It should be noted that those skilled in the art will understand, explicitly and implicitly, that the embodiments described in this application can be combined with other embodiments without conflict. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The terms "a," "an," "an," "the," and similar words used in this application do not indicate quantity limitation and can represent singular or plural. The terms "comprising," "including," "having," and any variations thereof used in this application are intended to cover non-exclusive inclusion; the terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific ordering of objects.

[0046] Reference Figure 1 , Figure 2 As shown in the figure, this application provides a method for evaluating the quality of roadside data, including:

[0047] Step S1: Preprocess the collected roadside data to obtain roadside data with normal trajectories.

[0048] In this embodiment, step S1 specifically includes:

[0049] Step S101: Sort the roadside data by time;

[0050] Step S102: Filter out noise data in the roadside data according to preset filtering conditions. The filtering conditions include filtering for trajectories whose duration is within a set time period.

[0051] Specifically, Roadside Units (RSUs) include data acquisition devices such as roadside laser / millimeter-wave radar and cameras. For example, they collect roadside data at a frequency of 10Hz, forming the trajectories of traffic targets within their sensing range. This roadside data includes information such as the latitude, longitude, speed, acceleration, and heading angle of the traffic targets at the timestamps of their travel trajectories.

[0052] The collected roadside data is sorted according to timestamps. This sorted data more accurately reflects the temporal changes in traffic flow, providing a reliable data foundation for subsequent quality assessments. After sorting the roadside data, preset filtering conditions are applied to filter out noisy data. Noisy data here mainly refers to trajectories with excessively short durations, i.e., trajectories whose duration is within a set time period (e.g., 1 second). The duration of a trajectory is determined by traversing all adjacent trajectory points and calculating the maximum and minimum event time differences. These trajectories may be generated due to equipment failure, signal interference, etc., and have no practical value for subsequent data analysis, therefore they need to be filtered out. Filtering out noisy data significantly reduces outliers and errors in the data, thereby reducing the computational burden of subsequent data processing and analysis, and improving the overall data quality and processing speed.

[0053] Step S2: Group the roadside data according to the unique identifier of the traffic target.

[0054] Specifically, a unique identifier (UUID) for each traffic object is extracted from the preprocessed roadside data. This identifier is typically automatically generated or assigned by the data acquisition system and is used to uniquely identify each perceived traffic object, such as a vehicle or pedestrian. A storage structure is created based on the extracted UUID. This storage structure can be a hash table, a dictionary, or any data structure capable of efficiently storing and retrieving key-value pairs. The key is the unique identifier of the traffic object, and the value is the set of roadside data corresponding to that identifier. By grouping, all roadside data related to the same traffic object can be centralized together to form a complete and coherent dataset, making it easier to perform computational operations such as trajectory determination and behavior analysis.

[0055] Step S3: Based on the grouped roadside data, calculate the quality evaluation index related to the driving status or dynamic parameters of traffic targets. The quality evaluation index includes at least one of the following: heading jump rate, speed jump rate, type jump rate, frequency jump rate, and position jump rate.

[0056] In this embodiment, the quality evaluation indicators include: heading jump rate, speed jump rate, type jump rate, frequency jump rate, and position jump rate.

[0057] Location jump rate: refers to the rate or magnitude of change in the location of a data point between consecutive timestamps.

[0058] Heading jump rate: refers to the rate or magnitude of change in heading (direction) of a data point between consecutive timestamps.

[0059] Rate of change of velocity: refers to the rate or magnitude of change of velocity of a data point between consecutive time stamps.

[0060] Type hopping rate: refers to the frequency with which the data type (such as vehicle type, pedestrian type, etc.) changes between consecutive timestamps.

[0061] Frequency hopping rate: refers to the change in the frequency of data point acquisition, such as whether the time interval between data points is stable.

[0062] In this embodiment, when calculating the heading jump rate, step S3 specifically includes:

[0063] Step SA301: For the trajectories formed by different types of traffic targets, calculate the change in heading angle between adjacent frames of roadside data;

[0064] Step SA302: If the change in heading angle exceeds the preset heading angle change threshold, it is determined to be a heading angle jump;

[0065] Step SA303: For roadside data collected by the same roadside acquisition device, calculate the heading jump rate based on the number of trajectories with heading angle jumps and the total number of trajectories.

[0066] Specifically, for roadside data with normal trajectories filtered out during preprocessing, these data are typically in frames. Heading angle information between adjacent frames is extracted from the roadside data, and the change in heading angle between adjacent frames is calculated. The heading angle is generally defined as the angle between the current direction of movement of the moving object and a fixed direction (such as true north).

[0067] The heading angle change calculated in step SA301 is compared with a preset heading angle change threshold. This heading angle change threshold is usually set according to the actual application scenario and the motion characteristics of the traffic target, and is used to distinguish between normal heading angle changes and abnormal jumps. If a heading angle change exceeds this threshold, it is determined to be a heading angle jump.

[0068] Because different types of traffic targets differ in their motion characteristics, the patterns and magnitudes of their heading angle changes may also vary. Therefore, it is necessary to calculate the heading angle change for each type of target separately. For example, according to different UUID types, if the UUID type is a motor vehicle trajectory, a heading angle change exceeding 30 degrees in adjacent frames is considered a heading angle jump; if the UUID type is a non-motor vehicle trajectory, a heading angle change exceeding 30 degrees in adjacent frames is considered a heading angle jump; and if the UUID type is a pedestrian trajectory, a heading angle change exceeding 90 degrees in adjacent frames is considered a heading angle jump.

[0069] For roadside data collected by the same roadside acquisition device, the number of tracks with heading angle jumps within a set time period (i.e., the number of times the heading angle jump was determined in step SA302) and the total number of tracks are counted. Then, the number of tracks with heading angle jumps is divided by the total number of tracks to obtain the heading jump rate. The calculation formula is as follows:

[0070]

[0071] Where t1 is the number of trajectories of a roadside data acquisition device that undergoes a change in heading angle within a set time period, and T is the total number of trajectories of the roadside data acquisition device within the set time period.

[0072] The heading jump rate is calculated to quantify the frequency of heading angle jumps in roadside data, in order to assess the continuity and stability of the trajectory of traffic objects in roadside data.

[0073] In this embodiment, when calculating the velocity jump rate, step S3 specifically includes:

[0074] Step SB301: For the trajectories formed by different types of traffic targets, calculate the velocity changes between adjacent frames in the roadside data;

[0075] Step SB302: If the change in heading angle exceeds the preset speed change threshold, it is determined to be a speed jump;

[0076] Step SB303: For roadside data collected by the same roadside acquisition device, calculate the speed jump rate based on the number of trajectories with speed jumps and the total number of trajectories.

[0077] Specifically, for trajectory data of traffic objects (such as vehicles and pedestrians) acquired by roadside data collection equipment, it is first necessary to perform continuous frame data analysis on each trajectory. For each pair of adjacent frames, the velocity change of the traffic object is calculated. The velocity change can be expressed by the formula Δv=v n+1 -v n Calculate, where v n+1 and v n The speeds of the (n+1)th frame and the nth frame are given.

[0078] Speed ​​jumps focus on rapid changes in speed, reflecting drastic changes in the motion state of a target object. A reasonable speed change threshold is set, determined by considering factors such as actual road conditions, target object type, and the accuracy of the acquisition equipment. The speed change Δv calculated in step SB301 is compared with this threshold. For each trajectory, the speed change between adjacent frames is calculated to see if it exceeds this threshold. If it does, the trajectory is marked as having a speed jump. To more accurately capture the patterns and characteristics of speed changes for each type of target object, the magnitude of speed change needs to be calculated separately for different types of traffic targets. For example, based on the UUID type, if the UUID is for a motor vehicle trajectory, a speed jump is defined as a change in speed exceeding 30 between adjacent frames; if the UUID is for a non-motorized vehicle trajectory, a speed jump is defined as a change in speed exceeding 20 between adjacent frames; and if the UUID is for a pedestrian trajectory, a speed jump is defined as a change in speed exceeding 6 between adjacent frames.

[0079] For all trajectory data collected by the same roadside data acquisition device within a certain period, the number of trajectories with speed jumps is counted. The speed jump rate is obtained by dividing the number of trajectories with speed jumps by the total number of trajectories, as calculated below:

[0080]

[0081] Where t2 is the number of trajectories of a roadside data acquisition device that experience speed jumps within a set time period, and T is the total number of trajectories of the roadside data acquisition device within the set time period.

[0082] The speed sag rate quantifies the stability of traffic flow over an entire road segment or a specific time period, providing an indicator for assessing road segment safety and traffic flow. A high speed sag rate may indicate complex traffic conditions, variable driving behavior, or errors in the data collection equipment.

[0083] In this embodiment, the calculation of the type jump rate, step S3, specifically includes:

[0084] Step SC301: Determine whether the type of the unique identifier between adjacent frames in each group of roadside data has changed. If it has changed, it is determined to be a type jump.

[0085] Step SC302: For roadside data collected by the same roadside acquisition device, calculate the type change rate based on the number of trajectories that have undergone type change and the total number of trajectories.

[0086] Specifically, for each set of roadside data, the system compares the types of each object in adjacent frames to see if they are consistent. If the unique identifier of an object changes from one type to another between adjacent frames, it is recorded as a type jump. For example, the previous frame shows a small car, and the next frame shows a large car, or a vehicle changes to a pedestrian.

[0087] For all trajectory data collected by the same roadside data acquisition device within a certain period, the number of trajectories that experienced type jumps is counted. The type jump rate is obtained by dividing the number of trajectories with type jumps by the total number of trajectories, and the calculation formula is as follows:

[0088]

[0089] Where t3 is the number of trajectories of a roadside data acquisition device that undergoes type change within a set time period, and T is the total number of trajectories of the roadside data acquisition device within the set time period.

[0090] In some cases, type transitions may be associated with abnormal events, such as misidentification or omission of data by the system. Therefore, by identifying and quantifying type transitions, errors in the data acquisition or processing process can be detected in a timely manner.

[0091] In this embodiment, when calculating the frequency jump rate, step S3 specifically includes:

[0092] Step SD301: Calculate the data acquisition frequency for each group of roadside data;

[0093] Step SD302: If the data acquisition frequency is not within the preset frequency threshold range, it is determined to be a frequency jump;

[0094] Step SD303: For roadside data collected by the same roadside acquisition device, calculate the frequency jump rate based on the number of trajectories with frequency jumps and the total number of trajectories.

[0095] Specifically, for each roadside data collection device, the collection frequency for each data set needs to be determined first. This is typically achieved by calculating the number of data frames collected per unit time (e.g., per second, per minute). For example, if a roadside device collects 360 frames of data in one minute, its data collection frequency is 6 frames / second. The collection frequency is then checked against a preset frequency threshold. If it falls outside this threshold, it is considered a frequency jump. For example, a collection frequency less than 8 frames / second or greater than 20 frames / second is considered a frequency jump.

[0096] For all trajectory data collected by the same roadside data acquisition device within a certain period, the number of trajectories with frequency jumps is counted. The frequency jump rate is obtained by dividing the number of trajectories with frequency jumps by the total number of trajectories, and the calculation formula is as follows:

[0097]

[0098] Where t4 is the number of trajectories of a roadside data acquisition device that experience frequency jumps within a set time period, and T is the total number of trajectories of the roadside data acquisition device within the set time period.

[0099] Data acquisition frequency is a crucial indicator of the stability of a data acquisition system. A stable data acquisition system should maintain a relatively constant acquisition frequency to ensure data continuity and consistency. Frequent and significant fluctuations in the acquisition frequency, causing frequency jumps, may indicate equipment failure, network latency, or other anomalies. Therefore, quantitatively assessing the severity of frequency jumps provides an optimization basis for equipment maintenance and data quality control.

[0100] In this embodiment, when calculating the position jump rate, step S3 specifically includes:

[0101] Step SE301: Calculate the distance and time difference between adjacent frames in each group of roadside data;

[0102] Step SE302: Calculate the positional transformation between adjacent frames based on the distance and time difference and convert it into velocity;

[0103] Step SE303: For trajectories formed by different types of traffic targets, if the position change exceeds the preset position change threshold, it is determined as a position jump;

[0104] Step SE304: For roadside data collected by the same roadside acquisition device, calculate the position jump rate based on the number of trajectories with position jumps and the total number of trajectories.

[0105] Specifically, for each set of roadside data, the distance and time difference between adjacent frames are calculated. The distance is calculated by determining the positional information of traffic objects in adjacent frames, while the time difference is the difference in timestamps between adjacent frames. After obtaining the distance and time difference between adjacent frames, these values ​​can be used to calculate the rate of positional change between adjacent frames, reflecting the speed of movement of traffic objects at different points in time.

[0106] Different types of traffic targets (such as vehicles, pedestrians, and bicycles) will have different trajectories. To detect position jumps, a preset position change threshold needs to be set for each type of traffic target. For example, according to different UUID types, if the UUID type is a motor vehicle trajectory, a position change exceeding 100 meters / second (equivalent to an instantaneous speed of 360 km / h) in adjacent frames is considered a position jump; if the UUID type is a non-motorized vehicle trajectory, a position change exceeding 70 meters / second (equivalent to an instantaneous speed of 252 km / h) in adjacent frames is considered a position jump; and if the UUID type is a pedestrian trajectory, a position change exceeding 10 meters / second (equivalent to an instantaneous speed of 36 km / h) in adjacent frames is considered a position jump.

[0107] For all trajectory data collected by the same roadside data acquisition device within a certain period, the number of trajectories with position jumps is counted. The position jump rate is obtained by dividing the number of trajectories with position jumps by the total number of trajectories, and the calculation formula is as follows:

[0108]

[0109] Where t5 is the number of trajectories of a roadside data acquisition device that change position within a set time period, and T is the total number of trajectories of the roadside data acquisition device within the set time period.

[0110] The main purpose of calculating the position jump rate is to quantify the sudden positional changes of traffic objects during their movement, so as to detect abnormal movement behavior of traffic objects in a timely manner.

[0111] Step S4: Based on the quality evaluation indicators, generate a data quality report and conduct a quality assessment of the roadside data according to the data quality report.

[0112] The quality of roadside data is comprehensively and systematically evaluated based on calculated multi-dimensional quality assessment indicators, generating a data quality report. To facilitate intuitive presentation of the report's content, charts and visualization tools can be used to display the distribution of each quality assessment indicator, the location of outliers, etc., and the overall quality of the dataset is assessed based on the proportion and severity of outliers. Data points exceeding thresholds are marked as outliers or low-quality data, with specific reasons provided (e.g., excessive location jumps, frequent heading changes, etc.). Specific data cleaning suggestions, such as deletion, correction, or smoothing, are proposed for identified outliers.

[0113] By summarizing the results of the data quality assessment, including the overall status of data quality, existing problems, and directions for improvement, the accuracy and consistency of the data are significantly improved. This ensures that the data used in downstream data analysis and model training is more reliable, providing a solid data foundation for research in intelligent transportation and autonomous driving. This is also the key reason why this application conducts a comprehensive data quality evaluation to implement a strict data quality control mechanism.

[0114] In summary, the roadside data quality evaluation method provided in this application preprocesses the collected roadside data to obtain roadside data with normal trajectories; groups the roadside data according to the unique identifier of traffic targets; calculates quality evaluation indicators related to the driving state or dynamic parameters of traffic targets based on the grouped roadside data, including at least one of the following: heading jump rate, speed jump rate, type jump rate, frequency jump rate, and position jump rate; generates a data quality report based on the quality evaluation indicators, and evaluates the quality of the roadside data according to the data quality report. This application conducts in-depth analysis and processing of roadside data, and through multi-dimensional quantitative evaluation, can comprehensively and accurately evaluate the quality of the collected roadside data. Through quality evaluation, high-quality data can be screened out, enhancing the application value of roadside data, ensuring the reliability of data in downstream data analysis and model training, and providing a solid data foundation for research on intelligent transportation and autonomous driving.

[0115] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0116] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0117] The electronic device includes: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the roadside data quality assessment method provided in any one or more of the above embodiments. Figure 3 An exemplary structural diagram of the electronic device is disclosed. For example... Figure 3As shown, the electronic device includes one or more processors 1101, a memory 1102, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0118] The electronic device may further include an input device 1103 and an output device 1104. The processor 1101, memory 1102, input device 1103, and output device 1104 may be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0119] Input device 1103 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0120] To provide interaction with the user, the electronic device can be a computer. The computer has: 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, the 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).

[0121] In this embodiment, a computer-readable medium stores a computer program / instruction, which, when executed by a processor, implements the roadside data quality assessment method provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0122] The memory 1102 can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0123] The memory 1102 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 1102 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, the memory 1102 may optionally include memory remotely located relative to the processor 1101, and these remote memories can be connected to the electronic device 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.

[0124] It should be noted that more specific examples of computer-readable storage media may include, but are not limited to: electrical connections having 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. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0125] Computer-readable storage media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0126] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, or similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0128] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0129] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A roadside data quality evaluation method, characterized by, The method comprises the following steps: preprocessing the collected roadside data to obtain the roadside data with normal trajectories; grouping the roadside data according to the unique identification number of the traffic target; calculating quality evaluation indexes related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data, wherein the quality evaluation indexes include at least one of the following: heading jump rate, speed jump rate, type jump rate, frequency jump rate and position jump rate; generating a data quality report based on the quality evaluation indexes, and performing quality evaluation on the roadside data according to the data quality report.

2. The roadside data quality evaluation method according to claim 1, characterized in that, The step of preprocessing the collected roadside data to obtain the roadside data with normal trajectories comprises the following steps: sorting the roadside data according to time; filtering out noise data in the roadside data according to a preset filtering condition, wherein the filtering condition includes filtering out trajectories with a duration within a set time period.

3. The roadside data quality evaluation method according to claim 1, characterized in that, When calculating the heading jump rate, the step of calculating quality evaluation indexes related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data comprises the following steps: calculating the change of heading angle between adjacent frame data in the roadside data for trajectories formed by different types of traffic targets; if the change of heading angle exceeds a preset heading angle change threshold, it is determined that the heading angle jumps; based on the number of trajectories with heading angle jumps and the total number of trajectories, the heading jump rate is calculated for the roadside data collected by the same roadside collection device.

4. The roadside data quality evaluation method of claim 1, wherein, When calculating the speed jump rate, the step of calculating quality evaluation indexes related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data comprises the following steps: calculating the change of speed between adjacent frame data in the roadside data for trajectories formed by different types of traffic targets; if the change of speed exceeds a preset speed change threshold, it is determined that the speed jumps; based on the number of trajectories with speed jumps and the total number of trajectories, the speed jump rate is calculated for the roadside data collected by the same roadside collection device.

5. The roadside data quality evaluation method of claim 1, wherein, When calculating the type jump rate, the step of calculating quality evaluation indexes related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data comprises the following steps: determining whether the type of unique identification number between adjacent frame data in each group of roadside data changes, and if it changes, it is determined that the type jumps; based on the number of trajectories with type jumps and the total number of trajectories, the type jump rate is calculated for the roadside data collected by the same roadside collection device.

6. The roadside data quality evaluation method of claim 1, wherein, When calculating the frequency jump rate, the step of calculating quality evaluation indexes related to the driving state or dynamic parameters of the traffic target based on the grouped roadside data comprises the following steps: calculating the data collection frequency of each group of roadside data; if the data collection frequency is not within the preset frequency threshold range, it is determined that the frequency jumps. The frequency jump rate is calculated based on the number of trajectories with frequency jump and the total number of trajectories for the roadside data collected by the same roadside collection device.

7. The roadside data quality evaluation method of claim 1, wherein, In the calculation of the position jump rate, the step of calculating the quality evaluation index related to the driving state or dynamic parameter of the traffic target based on the grouped roadside data comprises: calculating the distance and time difference between adjacent frame data in each group of roadside data; calculating the position transformation between adjacent frames and converting it into speed according to the distance and time difference; For trajectories formed by different types of traffic targets, if the position change exceeds a preset position change threshold, it is determined as position jump; The position jump rate is calculated based on the number of trajectories with position jump and the total number of trajectories for the roadside data collected by the same roadside collection device.

8. An electronic device, comprising: The electronic device comprises: one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the roadside data quality evaluation method according to any one of claims 1-7.

9. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the roadside data quality evaluation method according to any one of claims 1-7.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the roadside data quality evaluation method according to any one of claims 1-7.

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