Sensor test data processing method and device
By type annotation and cropping of sensor test data, the problem of low data analysis efficiency in the prior art is solved, and efficient data screening and analysis is achieved.
Patent Information
- Application Number
- CN202411805525.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-03-21
AI Technical Summary
Existing sensor test data processing methods cannot effectively screen and analyze data, resulting in low data analysis efficiency.
By typed the collected test data, test data containing data labels are generated, and data is cropped based on preset cropping rules to generate streamlined and highly correlated data.
It improves the analysis efficiency of sensor test data, provides streamlined and highly relevant data, and supports targeted analysis.
Smart Images
Figure CN119666048A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing, and in particular to a sensor test data processing method and device. Background Art
[0002] At present, the perception domain is an indispensable part of intelligent driving, and its detection performance directly affects the final presentation of intelligent driving functions. At present, sensor testing mainly relies on simulation, bench and real vehicle testing. Among them, simulation and bench testing are different from actual conditions, and real vehicle testing cannot introduce quantitative references, so there are certain errors.
[0003] The existing sensor test data processing method obtains a true value data set by extracting frames at a preset fixed interval, but does not further process the true value data. When used, it only compares the true value data with the tested data and outputs the comparison result. It is unable to filter data according to demand conditions for targeted analysis, and the data analysis efficiency is low. Summary of the invention
[0004] The present invention provides a sensor test data processing method and device to improve data analysis efficiency.
[0005] In order to solve the above technical problems, an embodiment of the present invention provides a sensor test data processing method, comprising:
[0006] Collecting first test data, and labeling the first test data based on data types to generate second test data; the second test data includes data labels;
[0007] The second test data is clipped based on the data label and preset clipping rules to generate third test data; the clipping rules include clipping conditions and clipping time; the clipping conditions include data type and sensor status; the clipping time includes the time range of the data.
[0008] The present invention generates second test data including data labels by labeling the collected first test data according to its type. Data labels are generated through automatic or manual labeling by the system to provide a basis for subsequent data classification and screening, and then the second test data is clipped using data labels and preset clipping rules, specific data types, sensor states or other predefined conditions, and the time range of data to generate third test data, thereby performing data segmentation according to specific conditions and time, thereby providing concise and highly relevant data for subsequent analysis and improving data analysis efficiency.
[0009] Further, the collecting the first test data based on the preset clock signal includes:
[0010] The truth value system and the system under test are synchronously collected based on a preset clock signal to obtain true value data of the truth value system and first test data of the system under test, wherein the true value data includes single laser radar data, multi-laser radar data and multi-sensor data; the first test data includes radar detection data, visual detection data and multi-sensor detection data.
[0011] Furthermore, the step of labeling the first test data based on the data type to generate the second test data includes:
[0012] The first test data is labeled based on the data type and event content to generate a data label; the data type includes positioning data, illumination data, vehicle speed data, yaw angle data and target type data; the data label includes time information and label content.
[0013] Furthermore, before the second test data is clipped based on the data label and the preset clipping rule, the method further includes:
[0014] A data classification standard table is set based on the data type and the event content, and the data classification standard table includes classification types and corresponding data types or classification events and corresponding event contents.
[0015] Furthermore, the step of clipping the second test data based on the data label and a preset clipping rule to generate third test data includes:
[0016] Based on matching the data tags with the data classification standard table, determining the classification type or classification event corresponding to each data tag;
[0017] The second test data is trimmed based on the division type, the division event, the trimming condition and the trimming time to generate third test data, wherein the trimming time includes the start and end time of the data label; and the trimming condition includes when the division type or division event contains multiple data labels, trimming the intersection of the multiple data labels.
[0018] Further, after generating the third test data, the method further includes:
[0019] Determine data requirements and data date based on test requirements, and match target partition types and corresponding target data types in the data partition standard based on the data requirements;
[0020] Acquire test data based on the data date, the target partition type and the target data type, and match and analyze the test data with the true value data.
[0021] Furthermore, when the test data and the true value data do not match each other properly, the true value data is updated based on a weighted average method.
[0022] In a second aspect, the present invention provides a sensor test data processing device, comprising: a data acquisition module, a data clipping module and a storage module;
[0023] The data acquisition module is used to collect first test data based on a preset clock signal, and to mark the first test data based on a data type to generate second test data; the second test data includes a data label;
[0024] The data clipping module is used to clip the second test data based on the data label and a preset clipping rule to generate third test data; the clipping rule includes a clipping condition and a clipping time;
[0025] The storage module is used to store the third test data in a preset designated path.
[0026] In a third aspect, the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the sensor test data processing method when executing the computer program.
[0027] In a fourth aspect, the present invention provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the sensor test data processing method. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 A schematic diagram of a flow chart of a sensor test data processing method provided by an embodiment of the present invention;
[0029] Figure 2 A schematic diagram of data acquisition of a truth value system and a system under test provided by an embodiment of the present invention;
[0030] Figure 3 A schematic diagram of a data structure of second test data provided by an embodiment of the present invention;
[0031] Figure 4 A data structure diagram of third test data provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0032] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0033] The terms "first" and "second" and the like in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.
[0034] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.
[0035] Example 1
[0036] See also Figure 1 , Figure 1 The present invention provides a flow chart of a sensor test data processing method according to an embodiment of the present invention. The present invention provides a sensor test data processing method, including steps 101 to 102, which are as follows:
[0037] Step 101: collecting first test data, and labeling the first test data based on data types to generate second test data; the second test data includes data labels;
[0038] In this embodiment, the collecting of first test data includes:
[0039] The truth value system and the system under test are synchronously collected based on a preset clock signal to obtain true value data of the truth value system and first test data of the system under test, wherein the true value data includes single laser radar data, multi-laser radar data and multi-sensor data; the first test data includes radar detection data, visual detection data and multi-sensor detection data.
[0040] In this embodiment, a truth value system is built based on scenario requirements, and the truth value system includes a single laser radar, multiple laser radars, and multiple sensors;
[0041] Building a system under test based on test requirements, the system under test includes radar detection, visual detection and multi-sensor detection;
[0042] Please refer to Figure 2 , Figure 2A schematic diagram of data acquisition of a truth value system and a system under test provided in an embodiment of the present invention.
[0043] In this embodiment, the truth value system construction plan can be a single laser radar plan, a multi-lidar plan, a multi-sensor fusion plan, etc. The truth value system data also includes some scene sensor data, such as positioning data, light and rain sensor data, vehicle speed data, yaw angle data, etc.
[0044] In this embodiment, according to the test scenario requirements, a suitable sensor combination is selected, for example: Single laser radar: suitable for simple environmental perception tasks, such as static obstacle detection. Multi-laser radar: suitable for complex environmental perception tasks, providing high-precision three-dimensional point cloud data. Multi-sensor fusion: combining multiple sensors such as laser radar, camera, millimeter wave radar, etc., to provide more comprehensive environmental perception.
[0045] In this embodiment, the selected sensor is installed on the test platform and calibrated to ensure the accuracy and consistency of the sensor data, and the data transmission channel is configured to ensure that the sensor data can be transmitted to the data processing unit in real time. The sensor data is preliminarily processed using data processing software, such as data correction, noise filtering, and other operations.
[0046] In this embodiment, the data collected by the true value system is used as a benchmark to verify the performance of the system under test. By selecting different sensor combinations, it can adapt to various test scenarios.
[0047] In this embodiment, the test requirements of the system under test are set up, which include radar detection, visual detection, and multi-sensor detection.
[0048] In this embodiment, a suitable sensor combination is selected according to the test requirements. For example: Radar detection: Use millimeter-wave radar for target detection and ranging. Visual detection: Use a camera for image acquisition and visual perception. Multi-sensor detection: Combine multiple sensors, such as radar, camera, lidar, etc., to perform multi-sensor data fusion.
[0049] In this embodiment, the selected sensor is installed on the test platform and calibrated to ensure the accuracy and consistency of the sensor data. The data transmission channel is configured to ensure that the sensor data can be transmitted to the data processing unit in real time. The sensor data is preliminarily processed using data processing software, such as data correction, noise filtering, etc.
[0050] In this embodiment, the system under test simulates the sensor configuration used in actual applications to ensure the practicality and reliability of the test results, and collects test data from multiple aspects through multi-sensor data fusion.
[0051] In this embodiment, the true value system data and the measured system data are collected simultaneously and timed by the same timing device.
[0052] In this embodiment, a high-precision clock signal source, such as a GPS timing device, is set to ensure the time synchronization of all sensors. All sensors are configured to synchronously start data acquisition when receiving the clock signal. In addition, in the data processing stage, the collected data is time-aligned to ensure the temporal consistency of the true value data and the measured data.
[0053] In this embodiment, clock signal synchronization is used to ensure that the true value system and the system under test collect data at the same time point, thereby improving the accuracy of data alignment. Time synchronization can also reduce errors caused by time offset and ensure the accuracy of test results.
[0054] In this embodiment, the test analyst can mark the test data during data collection or when playing back the data after data collection.
[0055] Please refer to Figure 3 , Figure 3 A data structure diagram of second test data provided by an embodiment of the present invention.
[0056] In this embodiment, the step of labeling the first test data based on the data type to generate the second test data includes:
[0057] The first test data is labeled based on the data type and event content to generate a data label; the data type includes positioning data, illumination data, vehicle speed data, yaw angle data and target type data; the data label includes time information and label content.
[0058] In this embodiment, the test data is first labeled and sorted, and the data labels are sourced from sensor data, vehicle body data, or tester marking data.
[0059] In this embodiment, the first test data is classified and labeled according to the type of data. Common data types include: Positioning data: including information such as the latitude, longitude, and altitude of the vehicle. Illuminance data: including information about the ambient light intensity. Vehicle speed data: including vehicle speed information. Yaw angle data: including vehicle yaw angle information. Target type data: including detected target types, such as vehicles, pedestrians, obstacles, etc.
[0060] In this embodiment, the data is annotated according to the event content recorded in the data. For example: Target detection event: records the detected target and its location, type and other information. Environmental change event: records events such as ambient lighting and weather changes. Vehicle behavior event: records vehicle acceleration, deceleration, steering and other behaviors.
[0061] In this embodiment, the system automatically identifies the data type and event content using an algorithm and generates a label. For special events, such as scenes with small amounts of data or requiring high-precision labeling, manual labeling can also be performed, or the data type can be identified based on the sensor type.
[0062] In this embodiment, a machine learning algorithm is used to automatically label the target type.
[0063] In this embodiment, the system labels the data of each time period according to the positioning data, illumination data, vehicle speed data, yaw angle data, target type data, etc. collected in the test data, such as high-speed road section, low illumination, straight driving, large curve, etc., and also labels the event labels manually labeled during the test. After the system labeling is completed, the test analyst can make notes, corrections, and add new data labels to the labels according to the actual situation. Among them, the data labels all contain the start and end time and label content, and the special event labels manually labeled include time plus label content.
[0064] In this embodiment, the timestamp of the data is recorded to ensure that each data point has accurate time information. The timestamp can be accurate to milliseconds to accurately align the data. The label content of the test data includes a detailed description of the data type and event content.
[0065] In this embodiment, different types of sensor data can be classified and stored by data type annotation, which is convenient for subsequent data processing and analysis. By event content annotation, important events such as target detection and environmental changes can be identified and recorded, providing a basis for subsequent event analysis. Thus, data tags with time information and tag content are generated, which facilitates data management and retrieval and improves the efficiency of data analysis.
[0066] Step 102: The second test data is clipped based on the data tag and preset clipping rules to generate third test data; the clipping rules include clipping conditions and clipping time; the clipping conditions include data type and sensor status; the clipping time includes the time range of the data.
[0067] In this embodiment, before the second test data is clipped based on the data label and the preset clipping rule, the method further includes:
[0068] A data classification standard table is set based on the data type and the event content, and the data classification standard table includes classification types and corresponding data types or classification events and corresponding event contents.
[0069] In this embodiment, different classification types are defined according to the test requirements, such as "positioning data", "illuminance data", "target detection data", "environmental change data", etc. The event type is a special event manually marked, such as a car accident event.
[0070] In this embodiment, a data classification standard table is created, which includes classification types and their corresponding data types, as well as classification events and their corresponding event contents. For example:
[0071] Table 1
[0072] Partition Type Data Types Divide events Event content Object detection data Vehicles, pedestrians Car Accident Car Accident 1, Car Accident 2 Illuminance data Daytime, Nighttime Environmental change data Heavy rain, light rain, moderate rain Road type data Tunnels, highways, expressways
[0073] In this embodiment, classification types, events and their corresponding conditions are defined to facilitate subsequent data processing and classification.
[0074] In this embodiment, the step of clipping the second test data based on the data label and a preset clipping rule to generate third test data includes:
[0075] Based on matching the data tags with the data classification standard table, determining the classification type or classification event corresponding to each data tag;
[0076] The second test data is trimmed based on the division type, the division event, the trimming condition and the trimming time to generate third test data, wherein the trimming time includes the start and end time of the data label; and the trimming condition includes when the division type or division event contains multiple data labels, trimming the intersection of the multiple data labels.
[0077] Please refer to Figure 4 , Figure 4 A data structure diagram of third test data provided by an embodiment of the present invention.
[0078] In this embodiment, the data type and event content are extracted from the data tag of the second test data. The extracted data type and event content are matched with the data classification standard table to determine the corresponding classification type and classification event. The data classification standard table is traversed to find items that match the data type and event content in the data tag. The classification type and classification event corresponding to each data tag are recorded.
[0079] In this embodiment, the start and end time of the cropping is determined according to the cropping conditions in the data partitioning standard table, and the data segments that meet the conditions are cropped from the second test data according to the cropping time to generate the third test data. When the partition type or partition event contains multiple data labels, the intersection of these labels is cropped. For example, when it is necessary to crop the data segments where it rains and the road type is a city road, only the data segments where it rains and the road type is a city road are cropped.
[0080] In this embodiment, for special event clipping, the data segment from before the event starts to after the event ends is clipped based on a preset time threshold ΔT.
[0081] In this embodiment, by setting up a data partitioning standard table and trimming rules, it is convenient to filter out data segments that meet specific conditions. At the same time, data can be trimmed according to different partitioning types and events, supporting refined analysis of specific scenarios or events, speeding up data analysis, and improving work efficiency.
[0082] In this embodiment, the third test data is stored in a preset designated path.
[0083] In this embodiment, after generating the third test data, the method further includes:
[0084] Determine data requirements and data date based on test requirements, and match target partition types and corresponding target data types in the data partition standard based on the data requirements;
[0085] Acquire test data based on the data date, the target partition type and the target data type, and match and analyze the test data with the true value data.
[0086] In this embodiment, according to the test objectives and requirements, specific data requirements are clarified, such as specific scenarios, specific events or specific data types that need to be analyzed. According to the test requirements, a suitable data date range is selected to ensure that the time range of the data meets the analysis requirements.
[0087] In this embodiment, the details of the data requirements are clarified, such as what types of data are required, the date range of the data, etc. According to the test requirements, the target division type that meets the requirements is found in the data division standard table. For example, if the test requirement is to analyze vehicle behavior, the target division type may be "vehicle speed data" or "yaw angle data". According to the target division type, the corresponding target data type is matched to ensure that the classification and labeling of the data meet the requirements.
[0088] In this embodiment, according to the determined data date range, target division type and target data type, the required data segments are retrieved from the stored third test data, and the retrieved data segments are extracted to ensure the integrity and accuracy of the data.
[0089] In this embodiment, the retrieved test data is time-aligned and matched with the true value data to ensure that the two sets of data have corresponding data points at the same time point. By comparing the test data with the true value data, performance analysis, error analysis, etc. are performed to evaluate the accuracy and performance of the system.
[0090] In this embodiment, by clarifying the data requirements, the test data that meets the conditions is screened out and matched with the true value data to improve the accuracy of data analysis.
[0091] In this embodiment, when the test data and the true value data do not match each other normally, the true value data is updated based on a weighted average method.
[0092] In this embodiment, when there is a time difference between the measured system data and the true value system data, the true value data is updated by weighted averaging, and then the measured system data is matched based on the updated true value data.
[0093] As a specific example of an embodiment of the present invention, if there is no matching true value data for the measured system data at time T, the true value data at time Tn and the data at time Tn+1 closest to time T are selected for calculation. Suppose the true value data at time Tn is Xn, and the true value data at time Tn+1 is Xn+1, then the true value data X matching time T=Xn[(T-Tn) / (Tn+1-Tn)].
[0094] In this embodiment, by comparing the test data with the true value data, identifying abnormal data points, and updating the true value data by weighted average method, the impact of single point abnormalities on the accuracy of the overall data can be reduced, thereby improving the reliability of the data.
[0095] The embodiment of the present invention also provides a sensor test data processing device, comprising: a data acquisition module, a data clipping module and a storage module;
[0096] The data collection module is used to collect first test data, and mark the first test data based on the data type to generate second test data; the second test data includes a data label;
[0097] The data clipping module is used to clip the second test data based on the data label and preset clipping rules to generate third test data; the clipping rules include clipping conditions and clipping time, the true value data includes single laser radar data, multi-laser radar data and multi-sensor data; the first test data includes radar detection data, visual detection data and multi-sensor detection data.
[0098] In this embodiment, the data acquisition module includes:
[0099] Building a truth value system based on scenario requirements, the truth value system includes a single laser radar, multiple laser radars, and multiple sensors;
[0100] Building a system under test based on test requirements, the system under test includes radar detection, visual detection and multi-sensor detection;
[0101] The true value system and the system under test are synchronously collected based on a preset clock signal to obtain true value data of the true value system and first test data of the system under test.
[0102] In this embodiment, the data acquisition module includes:
[0103] The first test data is labeled based on the data type and event content to generate a data label; the data type includes positioning data, illumination data, vehicle speed data, yaw angle data and target type data; the data label includes time information and label content.
[0104] In this embodiment, the data clipping module includes:
[0105] A data classification standard table is set based on the data type and the event content, and the data classification standard table includes classification types and corresponding data types or classification events and corresponding event contents.
[0106] In this embodiment, the data clipping module includes:
[0107] Based on matching the data tags with the data classification standard table, determining the classification type or classification event corresponding to each data tag;
[0108] The second test data is trimmed based on the division type, the division event, the trimming condition and the trimming time to generate third test data, wherein the trimming time includes the start and end time of the data label; and the trimming condition includes when the division type or division event contains multiple data labels, trimming the intersection of the multiple data labels.
[0109] In this embodiment, a test module is also included, which is used to:
[0110] Determine data requirements and data date based on test requirements, and match target partition types and corresponding target data types in the data partition standard based on the data requirements;
[0111] Acquire test data based on the data date, the target partition type and the target data type, and match and analyze the test data with the true value data.
[0112] In this embodiment, when the test data and the true value data do not match each other normally, the true value data is updated based on a weighted average method.
[0113] In this embodiment, the present invention ensures the time synchronization of the true value data and the test data through a unified clock signal, thereby aligning the data time, and annotating the collected first test data according to its type to generate second test data containing data labels. Data labels are generated through automatic or manual labeling by the system to provide a basis for subsequent data classification and screening, and then the second test data is cropped using the data labels and preset cropping rules to generate third test data. The cropping conditions in the cropping rules can be specific data types, sensor states, or other predefined conditions; the cropping time defines the time range of the data. Through cropping, the data is segmented according to specific conditions and time, thereby providing concise and highly relevant data for subsequent analysis, thereby improving the efficiency of true value data analysis.
[0114] In an embodiment of the present invention, a terminal device is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the above-mentioned sensor test data processing method when executing the computer program.
[0115] In an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored computer program. When the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned sensor test data processing method.
[0116] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.
[0117] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor, a memory, and a display. Those skilled in the art will appreciate that the above components are merely examples of the terminal device and do not constitute a limitation on the terminal device. The terminal device may include more or fewer components than the components, or may combine certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.
[0118] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.
[0119] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
[0120] Among them, if the module based on the processing of the multi-device access platform is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. Ordinary technicians in this field can understand and implement it without paying creative labor.
[0121] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.
Claims
1. A sensor test data processing method, characterized in that: include: Collecting first test data, and marking the first test data based on data types to generate second test data; The second test data includes a data label; The second test data is clipped based on the data label and a preset clipping rule to generate third test data; the clipping rule includes a clipping condition and a clipping time; The clipping conditions include data type and sensor status; The clipping time includes the time range of the data.
2. A sensor test data processing method as claimed in claim 1, characterized in that: The collecting of the first test data comprises: The truth value system and the system under test are synchronously collected based on a preset clock signal to obtain true value data of the truth value system and first test data of the system under test, wherein the true value data includes single laser radar data, multi-laser radar data and multi-sensor data; the first test data includes radar detection data, visual detection data and multi-sensor detection data.
3. A sensor test data processing method as claimed in claim 2, characterized in that: The step of labeling the first test data based on the data type to generate second test data includes: The first test data is labeled based on the data type and event content to generate a data label; the data type includes positioning data, illumination data, vehicle speed data, yaw angle data and target type data; the data label includes time information and label content.
4. A sensor test data processing method as claimed in claim 3, characterized in that: Before the second test data is trimmed based on the data label and the preset trimming rule, the method further includes: A data classification standard table is set based on the data type and the event content, and the data classification standard table includes classification types and corresponding data types or classification events and corresponding event contents.
5. A sensor test data processing method as claimed in claim 4, characterized in that: The step of clipping the second test data based on the data label and a preset clipping rule to generate third test data includes: Based on matching the data tags with the data classification standard table, determining the classification type or classification event corresponding to each data tag; The second test data is trimmed based on the division type, the division event, the trimming condition and the trimming time to generate third test data, wherein the trimming time includes the start and end time of the data label; and the trimming condition includes when the division type or division event contains multiple data labels, trimming the intersection of the multiple data labels.
6. A sensor test data processing method as claimed in claim 5, characterized in that: After generating the third test data, the method further includes: Determine data requirements and data date based on test requirements, and match target partition types and corresponding target data types in the data partition standard based on the data requirements; Acquire test data based on the data date, the target partition type and the target data type, and match and analyze the test data with the true value data.
7. A sensor test data processing method as claimed in claim 6, characterized in that: When the test data and the true value data do not match each other properly, the true value data is updated based on a weighted average method.
8. A sensor test data processing device, characterized in that: include: Data acquisition module, data cutting module and storage module; The data collection module is used to collect first test data, and mark the first test data based on data type to generate second test data; The second test data includes a data label; The data clipping module is used to clip the second test data based on the data label and a preset clipping rule to generate third test data; the clipping rule includes a clipping condition and a clipping time; The clipping conditions include data type and sensor status; The clipping time includes the time range of the data.
9. A terminal device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a sensor test data processing method as claimed in any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a sensor test data processing method according to any one of claims 1 to 7.