Truck formation scene data missing determination method and device

Abstract

The invention provides a scene data missing determination method and device for a truck formation, and relates to the technical field of truck formation, and the method comprises the steps: extracting a logic scene data set from a truck formation standard data set according to a logic rule corresponding to a test task; extracting a specific scene data set corresponding to each combined feature from the logic scene data set according to the combined features of the road type and the road form; and determining a target specific scene data set with missing scene data according to the data volume contained in the specific scene data set corresponding to each combined feature. According to the invention, the missing scene data is mined, and the reliability of the test result of the formation vehicles is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of truck platooning, and in particular to a method and device for determining missing scene data of a truck platoon. Background Art

[0002] Truck platooning is an advanced transportation technology that involves two or more trucks communicating via Vehicle-to-Vehicle (V2V) technology, transmitting real-time acceleration and deceleration information, speed, and position information of the leading vehicle to the following vehicle. Truck platooning also utilizes Advanced Driving Assistance Systems (ADAS), such as adaptive cruise control, lane departure warning, and automatic braking, to automatically control the following vehicle, allowing the trucks to travel in a close-spaced formation on the road.

[0003] Truck platooning technology is currently in the promotion and testing phase, requiring data collection from various operating scenarios to meet the testing requirements of platooning vehicles. However, due to the massive amount of collected data, it is difficult to identify missing data for certain scenarios, which reduces the reliability of platooning test results. Summary of the Invention

[0004] The present invention provides a method and device for determining missing scene data of a truck platoon, which are used to solve the defect in the prior art that it is difficult to discover missing scene data, realize the discovery of missing scene data, and improve the reliability of the test results of the platoon vehicles.

[0005] The present invention provides a method for determining missing scene data of a truck platoon, comprising: According to the logical rules corresponding to the test task, a logical scenario dataset is extracted from the truck platoon standard dataset; Extracting a specific scene dataset corresponding to each combined feature from the logical scene dataset based on the combined features of the road type and the road morphology; A target specific scene data set with missing scene data is determined according to the data amount contained in the specific scene data set corresponding to each combination feature.

[0006] In some embodiments, determining the target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combined feature includes: Arrange the data volume of the specific scene data sets corresponding to all combined features in descending order; The specific scene data set corresponding to the last one or more data amounts in the sequence is determined as the target specific scene data set.

[0007] In some embodiments, determining the target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combined feature includes: Comparing the amount of data contained in the specific scene data set corresponding to each combined feature with the corresponding data amount threshold; A specific scene data set containing a data volume less than a data volume threshold is determined as the target specific scene data set.

[0008] In some embodiments, after extracting the specific scene data set corresponding to each combination feature from the logical scene data set, the method further includes: According to the specific scene data set corresponding to each combination feature, a curve analysis graph corresponding to each specific scene data set is drawn; A playback analysis of the truck platoon is performed based on the curve analysis diagram corresponding to each specific scenario data set.

[0009] In some embodiments, before extracting the logical scenario dataset from the truck platoon standard dataset according to the logical rules corresponding to the test task, the method further includes: Obtain the original dataset of truck platooning based on the upload of vehicle communication units and manual copy upload; Preprocessing the truck platoon original data set to obtain a truck platoon preprocessed data set; Match and extract key data from the truck platoon preprocessing dataset according to regular expressions to obtain a truck platoon key dataset; The truck platoon key data set is standardized and converted to obtain the truck platoon standard data set.

[0010] In some embodiments, the logic rules corresponding to the test task include: scene definition rules, scene segment capture logic, test requirements, data requirements and evaluation logic.

[0011] The present invention also provides a device for determining missing scene data of a truck platoon, comprising: The first extraction module is used to extract a logical scenario dataset from the truck platooning standard dataset according to the logical rules corresponding to the test task; A second extraction module is used to extract a specific scene data set corresponding to each combined feature from the logical scene data set based on the combined features of road type and road morphology; The determination module is used to determine a target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combination of features.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for determining missing scene data of a truck formation as described above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for determining scene data missing for a truck formation.

[0014] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for determining missing scene data for a truck platoon.

[0015] The method and device for determining missing scene data for a truck platoon provided by the present invention extract a logical scene data set from a standard truck platoon data set according to the logical rules corresponding to the test task, thereby reducing the data volume of the data set. The logical scene data set is split into multiple specific scene data sets with smaller data volumes based on the combined characteristics of road type and road morphology. Based on the specific scene data sets, the target specific scene data set with missing scene data is determined, thereby discovering the missing scene data and improving the reliability of the test results of the platoon vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is one of the flow charts of the method for determining missing scene data for a truck platoon provided by the present invention.

[0018] Figure 2 This is the second flow chart of the method for determining missing scene data for a truck platoon provided by the present invention.

[0019] Figure 3 It is a structural diagram of the device for determining missing scene data of a truck platoon provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0022] Figure 1 FIG. 1 is a flow chart of a method for determining missing scene data of a truck platoon provided by the present invention. Figure 1 As shown, the present invention provides a method for determining missing scene data of a truck platoon, comprising: Step 110 : extracting a logical scenario dataset from the truck platoon standard dataset according to the logical rules corresponding to the test task.

[0023] Specifically, a test task is obtained, and the logical scenarios required by the test task are determined. Each logical scenario corresponds to a set of logical rules that can be recognized by a computer.

[0024] Exemplarily, the logical scenarios include: tunnel scenario, passing through the inspection area scenario, passing through the service area scenario, passing through the toll station scenario, communication abnormality scenario, entering and exiting the formation scenario, lane line detection scenario, road cone and other static object recognition scenario, formation operation scenario, emergency braking scenario, stable following scenario, social vehicle insertion scenario, signboard scenario, emergency lane parking scenario, positioning abnormality scenario, lane change scenario, dangerous scenario, acceleration operation scenario, deceleration operation scenario, signal light recognition scenario, traffic participant recognition scenario, etc.

[0025] Data that meets the logical rules corresponding to the test task is filtered out from the truck platoon standard dataset, and the filtered data is combined into a logical scenario dataset.

[0026] Step 120 : extracting a specific scene data set corresponding to each combined feature from the logical scene data set based on the combined features of the road type and the road shape.

[0027] Specifically, different road types and road morphologies are combined to form a combined feature that includes both road type and road morphology. For example, if road types include urban roads and highways, and road morphologies include straights, curves, and ramps, then the combined feature that includes both road type and road morphology includes urban road &&straight, urban road &&curve, urban road &&ramp, highway &&straight, highway &&curve, and highway &&ramp.

[0028] The logical scenario dataset represents the relationships between scene elements through parameter ranges in the state space, which are determined by probability distribution. The specific scenario dataset represents the relationships between scene elements by specifying the specific values of each parameter in the state space. The specific scenario dataset is a subset of the logical scenario dataset. For example, if the lane width in the logical scenario dataset is [2.3–3.5] m, the lane width in the specific scenario dataset is [3.2] m.

[0029] The data contained in the logical scene dataset is classified by combining features, and the specific scene dataset corresponding to each combined feature is extracted to observe the feature distribution of the logical scene dataset.

[0030] Step 130 : determining a target specific scene data set with missing scene data based on the data volume of the specific scene data set corresponding to each combination feature.

[0031] Specifically, the amount of data contained in the specific scene data sets corresponding to all combination features is analyzed to determine the data proportion of different combination features. The specific scene data sets corresponding to the combination features with the smaller data proportion are determined as the target specific scene data sets with missing scene data.

[0032] Targeted data collection is carried out based on the target specific scenario data set where scenario data is missing to ensure that each specific scenario process meets the test requirements. At the same time, it also provides users with the function of exporting scenario data sets, supports multiple formats and target selections, and meets the data requirements in different scenarios. Through rich configuration options and permission control, it ensures data security and convenience.

[0033] The method for determining missing scene data for truck platoons provided by the present invention extracts a logical scene data set from a standard truck platoon data set according to logical rules corresponding to a test task, thereby reducing the data volume of the data set. The logical scene data set is split into multiple specific scene data sets with smaller data volumes based on the combined characteristics of road type and road morphology. Based on the specific scene data sets, a target specific scene data set with missing scene data is determined, thereby discovering missing scene data and improving the reliability of the test results of platoon vehicles.

[0034] In some embodiments, the logic rules corresponding to the test task include: scenario definition rules, scenario segment capture logic, test requirements, data requirements, and evaluation logic.

[0035] Specifically, logical rules include: scenario definition rules, scenario segment capture logic, test requirements, data requirements, and evaluation logic. Scenario definition rules define the conditions for meeting logical scenarios, scenario segment capture logic defines the conditions for meeting logical scenario segments, test requirements define the conditions for starting a test, data requirements define the type of data to be acquired, and evaluation logic defines the quantitative conditions for performance evaluation.

[0036] For example, if the test task requires stable following of the scene, the logical rules corresponding to the test task are as follows: (1) Scenario definition rules The speeds of the vehicle test (VT) are 30km / h, 20km / h, and 10km / h respectively. The follow vehicle (FV) follows the lead vehicle (LV) in a platoon at a speed of 60km / h on a straight road, approaching the VT. The requirements are that the LV and FV can maintain the platoon state, the speeds of the FV and LV are synchronized with the VT speed, the distance between the platoon vehicles is kept within 16m, the lateral overlap rate should not be less than 80%, the platoon vehicles cannot collide with the VT, and they must continue to follow the VT for 100m.

[0037] (2) Scene segment capture logic Tracking Time (TT): The longitudinal distance between LV and the preceding vehicle is less than 100 m, and the preceding vehicle of LV is VT, and LV, FV, and VT are in the same lane, and the speeds of LV, FV, and VT are approximately the same.

[0038] Start Time (TS): First LV, FV speed is 60 km / h and VT speeds are 30 km / h, 20 km / h, and 10 km / h respectively.

[0039] End Time (TE): After the stable following time TT ends, the LV running distance is greater than 100m.

[0040] The time interval between the stable following time TT and the start time TS is less than 300 s, and the time interval between the stable following time TT and the end time TE is less than 300 s.

[0041] (3) Testing requirements Starting time TS: The vehicle speed tolerance is 5km / h, the VT speeds are 30km / h, 20km / h, and 10km / h respectively, and the LV and FV speeds are 60km / h.

[0042] (4) Data requirements Data requirements for FV: Query the vehicle chassis speed (Chassis Speed) measurement data (chassis_speedMps) table to obtain the distance between FV and LV, FV lateral deviation, and lane.

[0043] Data requirements for LV: Query the distance between LV and the preceding vehicle, LV lateral deviation, and lane in the chassis_speedMps table.

[0044] Data requirements for VT: query speed and lane.

[0045] (5) Evaluation logic Distance between FV and LV: The distance between FV and the preceding LV, control log, and time interval between FV and LV are less than or equal to 2s.

[0046] FV and LV stability: The absolute value of the speed, chassis log, and maximum acceleration of FV and LV is less than 3 or the standard deviation of the speed is less than 5.

[0047] FV and LV lateral overlap rate: the lateral deviation of FV and LV, control log FV_LV HOR is less than 80%.

[0048] No collision: The distance between LV, FV, and VT is greater than 10m.

[0049] The data is screened using the logical rules corresponding to the stable following scenario, and the dataset that meets the criteria is recorded as the stable following scenario dataset.

[0050] In some embodiments, determining a target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combined feature includes: Arrange the data volume of the specific scene data sets corresponding to all combined features in descending order; The specific scene data set corresponding to the last one or more data amounts in the sequence is determined as the target specific scene data set.

[0051] Specifically, the target scene dataset can be determined by arranging the data amounts contained in the specific scene datasets corresponding to all combined features in descending order. Missing scene data can be characterized as a small amount of data, so the specific scene dataset corresponding to the last one or more data amounts in the sequence is determined as the target specific scene dataset.

[0052] The method for determining missing scene data for a truck platoon provided by the present invention determines a target specific scene data set by arranging the data in descending order.

[0053] In some embodiments, determining a target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combined feature includes: Compare the data volume contained in the specific scene data set corresponding to each combined feature with the corresponding data volume threshold; A specific scenario data set containing a data volume less than a data volume threshold is determined as a target specific scenario data set.

[0054] Specifically, the method for determining the target scene data set can also be: pre-setting a data volume threshold corresponding to each combined feature, comparing the data volume contained in the specific scene data set corresponding to each combined feature with the corresponding data volume threshold, and determining the specific scene data set containing a data volume less than the data volume threshold as the target specific scene data set.

[0055] The method for determining missing scene data of a truck formation provided by the present invention determines the target specific scene data set by comparing the amount of data contained in the specific scene data set corresponding to each combined feature with the corresponding data amount threshold.

[0056] In some embodiments, after extracting the specific scene data set corresponding to each combination feature from the logical scene data set, the method further includes: According to the specific scene data set corresponding to each combination feature, draw a curve analysis chart corresponding to each specific scene data set; The truck platoon is replayed and analyzed based on the curve analysis chart corresponding to each specific scenario data set.

[0057] Specifically, the indicator analysis is displayed in units of scene processes. According to the specific scene data set corresponding to each combination feature, a graphical tool is used to use time as the horizontal axis and displacement distance, speed, acceleration, and distance to the preceding vehicle as the vertical axis to draw a curve analysis chart corresponding to each specific scene data set.

[0058] By using the curve analysis chart combined with the position relationship diagram of the vehicles in the truck formation, the truck formation is replayed and analyzed at each time.

[0059] Optionally, users can customize the playback speed to facilitate understanding and comparison of the relationship between the position changes of truck formations at different times and the distance, speed, acceleration, and distance to the preceding vehicle, providing an analysis tool for troubleshooting vehicle status at times of data anomalies.

[0060] Optionally, key indicators are extracted from the specific scenario data set corresponding to each combined feature, and displayed on a large indicator screen to track the achievement of business goals, identify anomalies and trend changes in the scenario data set, and provide data analysis support services for vehicle decision analysis.

[0061] The method for determining missing scene data for a truck platoon provided by the present invention performs playback analysis on the truck platoon by drawing a curve analysis diagram corresponding to each specific scene data set, thereby providing an analysis tool for checking the vehicle status at the moment of data anomaly.

[0062] In some embodiments, before extracting the logical scenario dataset from the truck platoon standard dataset according to the logical rules corresponding to the test task, the method further includes: Obtain the original dataset of truck platooning based on the upload of vehicle communication units and manual copy upload; Preprocess the truck platoon original dataset to obtain a truck platoon preprocessed dataset; Match and extract key data from the truck platoon preprocessing dataset using regular expressions to obtain the truck platoon key dataset; The truck platooning key dataset is standardized and transformed to obtain the truck platooning standard dataset.

[0063] Specifically, vehicle data upload methods can be categorized as either onboard communication units (ICUs) or manual copying. These units include Zigbee, Bluetooth, and cellular communication units. The original truck platoon dataset is obtained through these methods.

[0064] For example, the truck platooning raw data set includes positioning module data, control module data, chassis information data, platooning information data, lane detection data, obstacle detection data, manual annotation data, image data, vehicle-to-vehicle (V2V) data, test drive log data, truck lane annotation data, etc.

[0065] Optionally, after the video data is uploaded, an image frame extraction task will be triggered to convert the video into image format according to the frame rate, and target detection and labeling tasks will be performed on the image. The labeling detection results can be manually verified and modified, and the target detection and labeling dataset will be finally obtained and stored in the specified OSS directory.

[0066] The original truck platoon dataset is preprocessed, such as cleaning and removing duplicate records, erroneous data, and missing values, to obtain a truck platoon preprocessed dataset.

[0067] Key data matching and extraction are performed on the truck platoon preprocessing dataset according to regular expressions to obtain the truck platoon key dataset. To ensure processing speed, multi-threaded processing is used, and the truck platoon key datasets from different threads are merged into a unified dataset. The dataset is then converted into a normalized and standardized truck platoon standard dataset for downstream processing.

[0068] The method for determining missing scene data of a truck platoon provided by the present invention obtains a standard truck platoon data set by preprocessing the original truck platoon data set, matching and extracting key data, and performing standardization conversion, which is conducive to subsequent data extraction of the standard truck platoon data set.

[0069] Figure 2 This is the second flow chart of the method for determining missing scene data of a truck platoon provided by the present invention. Figure 2 As shown, the present invention provides a method for determining missing scene data for truck platooning, including data acquisition, data processing, logical scene data set acquisition, specific scene data set acquisition, indicator display and status monitoring. The specific process is as follows: Data acquisition: The original dataset of the truck platoon is obtained through uploading via the vehicle communication unit and manual copying.

[0070] Data processing: Preprocess the original truck platoon dataset, such as cleaning and removing duplicate records, erroneous data, and missing values to obtain the truck platoon preprocessed dataset; match and extract key data from the truck platoon preprocessed dataset according to regular expressions to obtain the truck platoon key dataset; merge the truck platoon key datasets from different threads into a unified dataset and convert it into a normalized and standardized truck platoon standard dataset.

[0071] Logical scenario dataset acquisition: According to the logical rules corresponding to the test task, the logical scenario dataset is extracted from the truck platooning standard dataset.

[0072] Acquisition of specific scene data sets: Based on the combined features of road type and road morphology, the specific scene data sets corresponding to each combined feature are extracted from the logical scene data sets.

[0073] Indicator display: Extract key indicators from the specific scenario data set corresponding to each combination of features, and display them on a large screen to track the achievement of business goals.

[0074] Status monitoring: Draw a curve analysis chart corresponding to each specific scenario data set. Use the curve analysis chart combined with the position relationship diagram of the vehicles in the truck formation to replay and analyze the truck formation at time to monitor the vehicle status.

[0075] The following describes a device for determining missing scene data for a truck formation provided by the present invention. The device for determining missing scene data for a truck formation described below and the method for determining missing scene data for a truck formation described above can refer to each other.

[0076] Figure 3 FIG. 1 is a schematic diagram of a structure of a device for determining missing scene data of a truck formation provided by the present invention. Figure 3As shown, the present invention provides a device for determining missing scene data of a truck platoon, comprising: A first extraction module 310 is configured to extract a logical scenario dataset from the truck platooning standard dataset according to the logical rules corresponding to the test task; A second extraction module 320 is configured to extract, from the logical scene dataset, a specific scene dataset corresponding to each combined feature based on the combined features of the road type and the road morphology; The determination module 330 is configured to determine a target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combination of features.

[0077] In some embodiments, the determining module 330 is specifically configured to: Arrange the data volume of the specific scene data sets corresponding to all combined features in descending order; The specific scene data set corresponding to the last one or more data amounts in the sequence is determined as the target specific scene data set.

[0078] In some embodiments, the determining module 330 is specifically configured to: Comparing the amount of data contained in the specific scene data set corresponding to each combined feature with the corresponding data amount threshold; A specific scene data set containing a data volume less than a data volume threshold is determined as the target specific scene data set.

[0079] In some embodiments, the apparatus further comprises: A drawing module, configured to draw a curve analysis diagram corresponding to each specific scene data set according to the specific scene data set corresponding to each combination feature; The analysis module is used to replay and analyze the truck platoon according to the curve analysis diagram corresponding to each specific scenario data set.

[0080] In some embodiments, the apparatus further includes an acquisition module, wherein the acquisition module is configured to: Obtain the original dataset of truck platooning based on the upload of vehicle communication units and manual copy upload; Preprocessing the truck platoon original data set to obtain a truck platoon preprocessed data set; Match and extract key data from the truck platoon preprocessing dataset according to regular expressions to obtain a truck platoon key dataset; The truck platoon key data set is standardized and converted to obtain the truck platoon standard data set.

[0081] In some embodiments, the logic rules corresponding to the test task include: scene definition rules, scene segment capture logic, test requirements, data requirements and evaluation logic.

[0082] It should be noted here that the scene data missing determination device for the above-mentioned truck formation provided by the present invention can implement all the method steps implemented by the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as the method embodiment in this embodiment will not be described in detail here.

[0083] Figure 4 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4 As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communications bus 440. The processor 410 may invoke logic instructions in the memory 430 to execute a method for determining missing scene data for a truck platoon. The method includes: extracting a logical scene dataset from a truck platoon standard dataset according to logic rules corresponding to a test task; extracting a specific scene dataset corresponding to each combined feature from the logical scene dataset based on combined features of road type and road form; and determining a target specific scene dataset for which scene data is missing based on the amount of data contained in the specific scene dataset corresponding to each combined feature.

[0084] Furthermore, the logic instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0085] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the truck formation scene data missing determination method provided by the above methods, the method including: extracting a logical scene data set from the truck formation standard data set according to the logical rules corresponding to the test task; extracting a specific scene data set corresponding to each combined feature from the logical scene data set based on the combined features of road type and road morphology; and determining the target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combined feature.

[0086] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining scene data missing for a truck formation provided by the above-mentioned methods, the method comprising: extracting a logical scene data set from a standard data set of a truck formation according to the logical rules corresponding to the test task; extracting a specific scene data set corresponding to each combined feature from the logical scene data set based on the combined features of road type and road morphology; and determining a target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combined feature.

[0087] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0088] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for determining missing scene data for a truck platoon, characterized in that: include: According to the logical rules corresponding to the test task, a logical scenario dataset is extracted from the truck platoon standard dataset; Extracting a specific scene dataset corresponding to each combined feature from the logical scene dataset based on the combined features of the road type and the road morphology; A target specific scene data set with missing scene data is determined according to the data amount contained in the specific scene data set corresponding to each combination feature.

2. The method for determining missing scene data of a truck platoon according to claim 1, characterized in that: The determining, based on the amount of data contained in the specific scene data set corresponding to each combined feature, a target specific scene data set with missing scene data, includes: Arrange the data volume of the specific scene data sets corresponding to all combined features in descending order; The specific scene data set corresponding to the last one or more data amounts in the sequence is determined as the target specific scene data set.

3. The method for determining missing scene data of a truck platoon according to claim 1, characterized in that: The determining, based on the amount of data contained in the specific scene data set corresponding to each combined feature, a target specific scene data set with missing scene data, includes: Comparing the amount of data contained in the specific scene data set corresponding to each combined feature with the corresponding data amount threshold; A specific scene data set containing a data volume less than a data volume threshold is determined as the target specific scene data set.

4. The method for determining missing scene data of a truck platoon according to claim 1, characterized in that: After extracting the specific scene data set corresponding to each combination feature from the logical scene data set, the method further includes: According to the specific scene data set corresponding to each combination feature, a curve analysis graph corresponding to each specific scene data set is drawn; A playback analysis of the truck platoon is performed based on the curve analysis diagram corresponding to each specific scenario data set.

5. The method for determining missing scene data of a truck platoon according to claim 1, characterized in that: Before extracting the logical scenario dataset from the truck platooning standard dataset according to the logical rules corresponding to the test task, the method further includes: Obtain the original dataset of truck platooning based on the upload of vehicle communication units and manual copy upload; Preprocessing the truck platoon original data set to obtain a truck platoon preprocessed data set; Match and extract key data from the truck platoon preprocessing dataset according to regular expressions to obtain a truck platoon key dataset; The truck platoon key data set is standardized and converted to obtain the truck platoon standard data set.

6. The method for determining missing scene data of a truck platoon according to claim 1, characterized in that: The logic rules corresponding to the test task include: scene definition rules, scene segment capture logic, test requirements, data requirements and evaluation logic.

7. A device for determining missing scene data for a truck formation, characterized in that: include: The first extraction module is used to extract a logical scenario dataset from the truck platooning standard dataset according to the logical rules corresponding to the test task; A second extraction module is used to extract a specific scene data set corresponding to each combined feature from the logical scene data set based on the combined features of road type and road morphology; The determination module is used to determine a target specific scene data set with missing scene data based on the amount of data contained in the specific scene data set corresponding to each combination of features.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for determining missing scene data of a truck platoon as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining missing scene data of a truck platoon as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining missing scene data of a truck platoon as claimed in any one of claims 1 to 6 is implemented.

Images