Road side sensing data dynamic evaluation method, equipment, medium and product

Abstract

The embodiment of the invention relates to the technical field of automatic driving, and discloses a roadside sensing data dynamic evaluation method and device, a medium and a product. According to the networked vehicle i d, the starting time, the ending time and the data source type, obtaining the original data of the networked vehicle in the corresponding time period; acquiring roadside sensing data based on the fusion geographic partition and the cloud fusion data corresponding to the running path of the network-connected vehicle; and carrying out time alignment and association on the network connection vehicle original data and the roadside sensing data, and calculating to obtain a roadside sensing data error. Through the whole hundred millisecond alignment technology, the time error is obviously reduced, the timestamps of the original data and the sensing data of the networked vehicle are more consistent, and the high-precision synchronization ensures the reliability and the accuracy of the data. By supporting multiple data sources such as Kafka and HBase, the system can flexibly cope with real-time and historical data requirements. The technical problem that the dynamic evaluation accuracy of the roadside sensing data is insufficient can be at least solved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a method, device, medium and product for dynamic evaluation of roadside perception data. Background Art

[0002] With the rapid development of autonomous driving technology, the role of roadside perception data in autonomous driving systems is becoming increasingly important. Roadside perception systems use sensors (such as cameras, millimeter-wave radars, and lidars) deployed along the road to obtain real-time traffic information about the vehicle's surrounding environment, including road conditions, vehicle location, speed, and pedestrian information. These data can not only assist the autonomous driving system in making real-time decisions, but also play an important role in vehicle navigation, safety, and traffic management.

[0003] In the development and testing of autonomous driving technology, it is particularly important to ensure the accuracy and reliability of roadside perception data. However, traditional roadside perception data evaluation methods rely on preset test scenarios, which often fail to fully reflect the complex situations in real road environments. In addition, due to the different data collection frequencies of different sensors, the timestamps may be inconsistent. These errors have an adverse effect on the quality evaluation results of perception data.

[0004] At present, the commonly used roadside perception data evaluation methods have the following shortcomings: In traditional methods, different sensors have different data collection frequencies and timestamps, which makes it difficult to accurately align the data, causing time errors, which in turn affects the accuracy of the evaluation results; existing methods usually rely on manual database queries for target data in a specific time period for comparison and association, which is time-consuming and difficult to achieve large-scale automated evaluation, limiting the breadth and efficiency of applications; in real driving environments, changes in vehicle driving paths and external environments cannot be fully simulated through static test scenarios, resulting in insufficient coverage of evaluation methods in real complex scenarios. Summary of the invention

[0005] One purpose of the present application is to provide a method, device, medium and product for dynamic evaluation of roadside perception data, at least to solve the technical problem of insufficient accuracy of dynamic evaluation of roadside perception data.

[0006] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0007] In the first aspect, some embodiments of the present application also provide a method for dynamic evaluation of roadside perception data, including obtaining the original data of the connected vehicle in the corresponding time period according to the connected vehicle ID, start time, end time and data source type; obtaining roadside perception data based on the fused geographical partition and cloud fusion data corresponding to the connected vehicle's driving path; time aligning and correlating the original data of the connected vehicle and the roadside perception data, and calculating the roadside perception data error.

[0008] In a second aspect, some embodiments of the present application further provide an electronic device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0009] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0010] In a fourth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the method described above when executed by a processor.

[0011] Compared with the related art, the solution provided in the embodiment of the present application utilizes a connected vehicle (true value vehicle) with a high-precision positioning function to dynamically and automatically evaluate the roadside perception data. Through the hundred-millisecond time alignment and cloud data fusion technology, the time and spatial information of the connected vehicle data and the roadside perception data can be accurately matched, thereby greatly improving the accuracy of data evaluation. In addition, it also supports the flexible selection of multiple data sources, automatically obtains the original data of the connected vehicle and the roadside perception data, avoids manual intervention, and realizes large-scale automatic capture and association processing of data. It can more accurately and comprehensively evaluate the quality of roadside perception data, provide strong data support for the development and testing of autonomous driving systems, and help improve the safety and reliability of autonomous driving technology in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0013] Figure 1 A schematic diagram of a flow chart of a method for dynamically evaluating roadside perception data provided according to an embodiment of the present application;

[0014] Figure 2The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, ordinary technicians in this field can

[0016] First embodiment

[0017] The first embodiment of the present application relates to a method for dynamically evaluating roadside perception data. Figure 1 As shown, the method may include the following steps:

[0018] S101, acquiring the original data of the connected vehicle in the corresponding time period according to the connected vehicle ID, start time, end time, and data source type;

[0019] S102, acquiring roadside perception data based on the fused geographical partition corresponding to the driving path of the connected vehicle and the cloud fusion data;

[0020] S103, time-aligning and associating the original data of the connected vehicle and the roadside perception data, and calculating the roadside perception data error.

[0021] The following is a detailed description of each of the above steps.

[0022] For step S101, the original data of the connected vehicle in the specified time period is obtained through the ID of the connected vehicle, the start time and end time of data extraction, and the specified data source type (such as Kafka real-time data or HBase historical data). The original data of the connected vehicle usually includes high-precision positioning information, speed, acceleration and other data related to the vehicle status.

[0023] The accuracy and flexibility of the data source are ensured. By supporting two data source types, Kafka and HBase, it can meet the needs of real-time data extraction and historical data analysis. This flexible data source selection enhances the applicability of the method and is suitable for different application scenarios. At the same time, by acquiring raw data within a specific time range, the redundancy of data extraction is reduced, making subsequent data alignment and association processing more efficient.

[0024] For step S102, after obtaining the original data of the connected vehicle, the fused geographical partition that the connected vehicle passes through is determined according to the driving path of the connected vehicle, and the roadside perception data corresponding to the geographical partition is obtained in the cloud fused data. The fused geographical partition is formed by dividing the map into polygonal areas of a certain distance, which is used to manage and store sensor data in the area.

[0025] Ensure that the roadside perception data associated with the connected vehicle path can be captured dynamically and in real time, so that the evaluation process can cover the actual driving path. Compared with traditional fixed test scenarios, dynamic data capture improves the comprehensiveness and practical reflectivity of data acquisition, and can more realistically reflect the performance of the roadside perception system in complex road environments. In addition, by associating the driving path of the connected vehicle with the roadside perception data in the partition, it helps with subsequent time alignment and error calculation, ensuring data matching and consistency.

[0026] For step S103, after obtaining the original data of the connected vehicle and the roadside perception data, the two types of data are time-synchronized using the whole 100 millisecond alignment technology. After time alignment, the corresponding connected vehicle data and roadside perception data are associated, and the errors in position, speed, and heading between the two are calculated. Through the whole 100 millisecond time alignment technology, the data of the connected vehicle and the roadside sensor are highly synchronized in time, reducing the time error caused by different data acquisition frequencies. The correlation calculation after time alignment can accurately measure the error of the roadside perception data, specifically including the positioning error, speed error, and heading error of the connected vehicle. These error data provide a quantitative basis for the accuracy evaluation of the roadside perception system, which helps to identify the source of deviation of the perception data and optimize the system. Through this automated error calculation, the automated evaluation of large-scale data can be achieved, which significantly improves the evaluation efficiency and accuracy.

[0027] It is not difficult to find that compared with related technologies, the solution provided in the embodiment of the present application realizes efficient evaluation of roadside perception data through flexible data acquisition, dynamic partition data capture and high-precision time alignment technology. Compared with traditional methods, accurate time synchronization is achieved through the alignment technology of whole milliseconds, making the evaluation results more reliable; dynamic capture of partition data corresponding to the driving path of the connected vehicle can reflect the complex situation in the actual road environment; data capture, alignment and error calculation can be completed automatically, avoiding manual operation and improving evaluation efficiency and processing scale. It provides a practical and efficient solution for the evaluation of roadside perception systems, which can enhance the accuracy and reliability of autonomous driving data in practical applications.

[0028] Second embodiment

[0029] The second embodiment of the present application relates to a method for dynamically evaluating roadside perception data. The second implementation is an improvement on the first embodiment, and the specific improvements are:

[0030] The Kafka data source is used to obtain real-time raw data of connected vehicles. The HBase data source is used to obtain historical raw data of connected vehicles.

[0031] Based on two data source types, Kafka and HBase, the raw data of connected vehicles can be flexibly acquired to meet the needs of real-time and historical data. First, the choice of data source is determined based on the input connected vehicle ID, the start and end time of data acquisition, the data source type and other parameters: when Kafka is selected, the connected vehicle data in the Kafka message queue is subscribed in real time, and the real-time data fields such as positioning, speed, acceleration, etc. that match the selected connected vehicle ID and time range are filtered out, and these data are sampled according to the timestamp accuracy of 100 milliseconds; when HBase is selected, the historical records that match the specified connected vehicle ID and time range are queried in the HBase database, the relevant data fields are extracted, and the timestamp alignment and precision processing are performed to ensure data consistency. Subsequently, the acquired real-time or historical data is stored in the cache area for preprocessing, such as removing outliers and interpolation processing, to ensure data quality, thereby providing high-precision raw data of connected vehicles for subsequent steps such as fusion geographic partition matching, data time alignment and error calculation.

[0032] The time alignment includes: time aligning the original data of the connected vehicle and the roadside perception data through the hundred-millisecond alignment technology.

[0033] The time synchronization processing of the original data of the connected vehicle and the roadside perception data is achieved through the whole 100 millisecond alignment technology to ensure high-precision matching of the two types of data. First, after obtaining the original data of the connected vehicle and the roadside perception data, the timestamps of the two types of data are extracted and converted into a unified whole 100 millisecond format. For the connected vehicle data, the timestamp is accurate to the nearest whole 100 millisecond value. For example, if the timestamp of a data point is 12:00:00.345 seconds, the timestamp is aligned to 12:00:00.300 seconds or 12:00:00.400 seconds, so that it can be matched with the roadside perception data at the same time accuracy.

[0034] Next, check the timestamps of the roadside perception data one by one and align them to the corresponding whole 100 milliseconds to ensure that the time accuracy of the connected vehicle data and the roadside perception data is consistent at each time point. For data points that do not fully match, estimate the missing data by interpolation or nearest neighbor value to ensure the integrity of the time alignment. After the alignment is completed, a matching data table is generated in which the connected vehicle data and the roadside perception data are paired at the same whole 100 millisecond timestamp, laying the foundation for subsequent error calculation and data association analysis. By aligning to whole 100 milliseconds, the time error caused by differences in data collection frequency can be significantly reduced, thereby improving the accuracy and reliability of the evaluation results.

[0035] Acquiring roadside perception data includes: based on the driving path of the connected vehicle, determining the fused geographical partition corresponding to its trajectory, and obtaining a set of perception data for each fused geographical partition; extracting the roadside perception data within the fused geographical partition from the cloud fusion data, and spatially matching it with the path of the connected vehicle.

[0036] The process of obtaining roadside perception data determines the fused geographic partition corresponding to the trajectory of the connected vehicle through the driving path of the connected vehicle, and dynamically obtains the perception data set in each partition. First, according to the real-time driving path of the connected vehicle, multiple fused geographic partitions that intersect with the path are divided on the map, and a partition list is generated. Each fused geographic partition is identified by a unique ID, and its range is usually determined by the division method of road nodes and driving areas.

[0037] Next, for each fused geographic partition that the connected vehicle passes through, the roadside perception data in the partition is extracted from the cloud fusion data according to its corresponding time range. The fusion data contains the perception results of various sensors in the area, such as the location, speed, direction and other information of objects collected by road cameras, lidar, and millimeter-wave radar. These partition data sets are spatially matched with the trajectory of the connected vehicle to ensure that every moment on the connected vehicle's driving path can be associated with the perception data of the partition at that moment.

[0038] Through the above process of dynamically acquiring the fused geographic partition data that the connected vehicle has passed through, a wide coverage of the partition data is achieved, avoiding the limitations of static data. Finally, the fused data obtained from the query and the roadside perception data are integrated so that the perception data in each fused geographic partition can correspond to the path of the connected vehicle, thereby improving the comprehensiveness and accuracy of the data and providing reliable data support for subsequent evaluation steps.

[0039] The timestamps of the original data of the connected vehicle and the roadside perception data are aligned with an accuracy of whole 100 milliseconds; and a corresponding relationship between the original data of the connected vehicle and the corresponding roadside perception data is automatically established based on the aligned timestamps.

[0040] The calculated roadside perception data error includes: the connected vehicle original data includes the connected vehicle positioning data, the connected vehicle speed data and the connected vehicle heading data; calculating the positioning error between the connected vehicle positioning data and the roadside perception data; calculating the speed error between the connected vehicle speed data and the roadside perception data; calculating the heading error between the connected vehicle heading data and the roadside perception data.

[0041] By comparing the original data of the connected vehicle with the roadside perception data, the error of the roadside perception data is calculated to evaluate its accuracy and reliability. Specifically, the original data of the connected vehicle includes high-precision positioning data, speed data, and heading data, which are used as the "true value" benchmark for error calculation with the roadside perception data.

[0042] Extract the connected vehicle’s positioning data (such as precise coordinates provided by GPS or inertial navigation systems) and compare it with the location information in the roadside perception data at the corresponding timestamp. By calculating the spatial distance difference between the two, the system obtains the positioning error, which reflects the accuracy of the roadside perception system in position identification. Extract the connected vehicle’s speed data and compare it with the target speed value in the roadside perception data at the corresponding time point. By calculating the difference between the connected vehicle’s speed and the roadside perception speed, the speed error is obtained, thereby judging the accuracy of the roadside perception system in detecting dynamic target speeds. Extract the connected vehicle’s heading data and compare it with the heading information in the roadside perception data. By calculating the angle difference between the two, the heading error is obtained, which is used to evaluate the accuracy of the roadside perception system in identifying the target heading.

[0043] Through the comprehensive calculation of the above positioning error, speed error and heading error, the accuracy of the roadside perception data can be comprehensively evaluated, and possible deviations and deficiencies of the roadside perception system can be identified. These error data not only provide a basis for system performance optimization, but also can dynamically calibrate and adjust the autonomous driving data through statistical analysis of the error results to ensure the reliability of the perception data.

[0044] It is not difficult to find that in the embodiment of the present application, the time error is significantly reduced through the hundred-millisecond alignment technology, making the timestamps of the original data of the connected vehicle and the perception data more consistent. This high-precision synchronization ensures the reliability and accuracy of the data. By supporting multiple data sources such as Kafka and HBase, the system can flexibly respond to real-time and historical data needs. This flexibility enhances the adaptability and practicality of the system and meets different application scenarios. Automated data processing avoids manual intervention and improves efficiency and accuracy. The system quickly performs data association and error calculation, provides reliable evaluation results, and reduces human errors. Real-time capture and processing of the association between the dynamic driving process of the connected vehicle and the perception data improves data accuracy and arbitrariness. This dynamic data acquisition ensures that the evaluation results can promptly reflect the data evaluation of roadside perception in various areas and situations.

[0045] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0046] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0047] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 2 An exemplary structural diagram of the electronic device is disclosed. Figure 2 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0048] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0049] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. 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.

[0050] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0051] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided by any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0052] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0053] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0054] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0055] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs 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 technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0056] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0057] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0058] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive so lid state di sk (SSD)), etc.

[0059] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0060] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0061] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A method for dynamic evaluation of roadside perception data, characterized in that: The method comprises: According to the connected vehicle ID, start time, end time, and data source type, obtain the original data of the connected vehicle in the corresponding time period; Obtain roadside perception data based on the fused geographic partitions and cloud fusion data corresponding to the connected vehicle's driving path; The original data of the connected vehicle and the roadside perception data are time-aligned and associated, and the roadside perception data error is calculated.

2. The method according to claim 1, characterized in that The data source types include: The Kafka data source is used to obtain real-time raw data of connected vehicles. The HBase data source is used to obtain historical raw data of connected vehicles.

3. The method according to claim 2, characterized in that The connected vehicle original data includes connected vehicle positioning data, connected vehicle speed data and connected vehicle heading data.

4. The method according to claim 3, characterized in that The time alignment includes: time aligning the original data of the connected vehicle and the roadside perception data through the hundred-millisecond alignment technology.

5. The method according to claim 4, characterized in that The acquiring of roadside perception data comprises: Based on the driving path of the connected vehicle, determine the fused geographical partition corresponding to its trajectory, and obtain the perception data set of each fused geographical partition; The roadside perception data within the fused geographical partition is extracted from the cloud fusion data, and spatially matched with the path of the connected vehicle.

6. The method according to claim 1, characterized in that The method comprises: The timestamps of the original data of the connected vehicle and the roadside perception data are aligned with an accuracy of whole 100 milliseconds; and a corresponding relationship between the original data of the connected vehicle and the corresponding roadside perception data is automatically established based on the aligned timestamps.

7. The method according to any one of claims 1 to 6, characterized in that: The calculated roadside perception data error includes: The connected vehicle raw data includes connected vehicle positioning data, connected vehicle speed data and connected vehicle heading data; Calculate the positioning error between the connected vehicle positioning data and the roadside perception data; calculate the speed error between the connected vehicle speed data and the roadside perception data; calculate the heading error between the connected vehicle heading data and the roadside perception data.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

9. A computer readable medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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