Data processing method and device, computer equipment, storage medium and program product

Abstract

The invention relates to the technical field of computers, and discloses a data processing method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring first driving data of a traffic participant of a current road and second driving data of an own vehicle; wherein the first driving data comprises a first identifier; determining a first track corresponding to each piece of first driving data and a second track corresponding to the second driving data of the current road according to the first driving data and the second driving data; determining a target trajectory corresponding to the second trajectory from the plurality of first trajectories, and determining a first identifier corresponding to the target trajectory as an own vehicle identifier of the own vehicle; and determining an index coefficient corresponding to the own vehicle according to the own vehicle identifier of the own vehicle.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to a data processing method, apparatus, computer device, storage medium, and program product. Background Art

[0002] Currently, according to the actual application scenarios of intelligent connected vehicles, a simulation scenario including elements such as roads, traffic signals, obstacles, and other vehicles is constructed. On the basis of the simulation scenario, an intelligent connected vehicle simulation system for V2X (Vehicle-to-Everything) is constructed. This system can simulate the information interaction process between intelligent connected vehicles and other traffic participants, including vehicle-to-vehicle communication, vehicle-to-infrastructure communication, etc.

[0003] However, although the simulation system can simulate various traffic scenarios and vehicle behaviors, due to factors such as technical level and computing power, it is still unable to fully reproduce the real road environment, resulting in inconsistent performance of intelligent connected vehicles in the actual road environment with the simulation test results, thus affecting their safety and reliability. Summary of the Invention

[0004] In view of this, the present invention provides a data processing method, apparatus, computer device, storage medium, and program product.

[0005] In a first aspect, the present invention provides a data processing method, which includes: obtaining first driving data of traffic participants on the current road and second driving data of the host vehicle; wherein, the first driving data includes a first identifier; determining, according to the first driving data and the second driving data, a first trajectory corresponding to each first driving data on the current road and a second trajectory corresponding to the second driving data; determining a target trajectory corresponding to the second trajectory from multiple first trajectories, and determining the first identifier corresponding to the target trajectory as the host vehicle identifier of the host vehicle; and determining an index coefficient corresponding to the host vehicle according to the host vehicle identifier of the host vehicle.

[0006] The data processing method provided in this embodiment can instantaneously reflect the real situation of the current road and dynamically adjust the recognition and tracking of traffic participants by obtaining and analyzing the driving data of traffic participants and the host vehicle. Moreover, by determining the trajectory corresponding to each driving data and finding the target trajectory matching the host vehicle trajectory, decisions can be made based on actual data rather than relying on the preset model of the simulation system. This method is closer to reality and can more accurately reflect the performance of intelligent connected vehicles in the actual road environment.

[0007] Meanwhile, according to the host vehicle identifier of the host vehicle, the index coefficient corresponding to the host vehicle can be determined, so that corresponding strategy adjustments can be made for different index coefficients of the host vehicle.

[0008] In a possible implementation, determining an index coefficient corresponding to the host vehicle according to the host vehicle identifier of the host vehicle includes: determining the generation time of the first driving data corresponding to the host vehicle identifier; determining the reception time of the first driving data corresponding to the host vehicle identifier obtained; determining the time difference between the generation time and the reception time; determining the type of the host vehicle, the source of the first driving data, and the host vehicle position index according to the host vehicle identifier; and determining the index coefficient corresponding to the host vehicle according to the type of the host vehicle, the source of the first driving data, the host vehicle position index, and the time difference.

[0009] The data processing method provided in this embodiment can evaluate the data transmission efficiency and latency by determining the generation time and reception time of the first driving data and calculating the time difference between them, timely discover problems in data transmission, and thus improve the timeliness of data processing.

[0010] At the same time, by comprehensively considering factors such as the type of the host vehicle, the source of the driving data, the position index, and the time difference, more refined index coefficients can be determined. These index coefficients can provide more comprehensive information support for evaluating aspects such as the driving performance, safety, and data transmission quality of the host vehicle.

[0011] In a possible implementation, determining an index coefficient corresponding to the host vehicle according to the type of the host vehicle, the source of the first driving data, the host vehicle position index, and the time difference includes: determining an index threshold according to the source of the first driving data and the type of the host vehicle; where the index threshold includes: a position index threshold and a difference threshold; determining a host vehicle position index coefficient according to the host vehicle position index and the position index threshold; determining a time difference coefficient according to the time difference and the difference threshold; and determining the index coefficient corresponding to the host vehicle according to the host vehicle position index coefficient, the time difference coefficient, the source of the first driving data, and the type of the host vehicle.

[0012] The data processing method provided in this embodiment can set the position index threshold and the difference threshold more accurately by comprehensively considering the source of the first driving data and the type of the host vehicle. When the source of the first driving data or the type of the host vehicle changes, the index threshold can be automatically adjusted, and then the index coefficient can be adjusted. This intelligent adjustment method enables the system to flexibly respond to different situations and improves the flexibility of decision-making.

[0013] At the same time, by comprehensively considering multiple dimensions such as the host vehicle position index coefficient, the time difference coefficient, the source of the first driving data, and the type of the host vehicle, a more comprehensive analysis of the driving state of the host vehicle can be performed.

[0014] In a possible implementation, obtaining the second driving data of the vehicle itself includes: obtaining a preset trajectory; where the preset trajectory is used to indicate the driving route of the vehicle itself on the current road; when the vehicle itself drives according to the preset trajectory, obtaining the second driving data of the vehicle itself; where the second driving data of the vehicle itself includes: driving speed, driving acceleration, time, longitude and latitude, and heading angle.

[0015] In the data processing method provided in this embodiment, since the corresponding trajectories of traffic participants are all relatively regular trajectories during the driving process on the current road, and the vehicle itself drives on the current road according to the preset trajectory, the trajectory obtained by the vehicle itself can be easily determined from multiple traffic participant trajectories.

[0016] In a possible implementation, the first driving data includes: first longitude and latitude, and the second driving data includes: second longitude and latitude; where determining the first trajectory corresponding to each first driving data and the second trajectory corresponding to the second driving data of the current road according to the first driving data and the second driving data includes: obtaining the first longitude and latitude and the second longitude and latitude collected at each preset time point; respectively performing coordinate conversion on the first longitude and latitude and the second longitude and latitude to determine the first plane coordinates corresponding to the first longitude and latitude and the second plane coordinates corresponding to the second longitude and latitude; determining the first position corresponding to each preset time point according to the first plane coordinates; determining the second position corresponding to each preset time point according to the second plane coordinates; connecting the first positions corresponding to each preset time point to form the first trajectory; connecting the second positions corresponding to each preset time point to form the second trajectory.

[0017] In the data processing method provided in this embodiment, by converting longitude and latitude into plane coordinates, the influence of the earth's curvature on position calculation can be eliminated, making position determination more accurate.

[0018] At the same time, by obtaining the longitude and latitude data collected at each preset time point, the continuity of the trajectory can be ensured. The position corresponding to each time point is calculated based on accurate longitude and latitude data, which helps to form a continuous and accurate trajectory. And connecting the positions corresponding to each preset time point can form an intuitive trajectory map for trajectory analysis and visual display.

[0019] In a possible implementation, determining the target trajectory corresponding to the second trajectory from multiple first trajectories includes: obtaining the first time stamp corresponding to each first trajectory among the multiple first trajectories and the second time stamp corresponding to the second trajectory; determining the target time stamp that is the same as the second time stamp from the multiple first time stamps; determining the target trajectory that is the same as the second trajectory from the first trajectory corresponding to the target time stamp.

[0020] The data processing method provided in this embodiment first determines a target timestamp that is the same as the second timestamp through a timestamp, which can exclude the influence of trajectories at other different times, so as to more accurately determine a target trajectory that is the same as the second trajectory.

[0021] In a second aspect, the present invention provides a data processing device, which includes: an acquisition module for acquiring first driving data of traffic participants on the current road and second driving data of the vehicle itself; wherein, the first driving data includes a first identifier; a first determination module for determining, according to the first driving data and the second driving data, a first trajectory corresponding to each first driving data on the current road and a second trajectory corresponding to the second driving data; a second determination module for determining a target trajectory corresponding to the second trajectory from multiple first trajectories and determining the vehicle identifier of the vehicle itself according to the first identifier corresponding to the target trajectory; a third determination module for determining an index coefficient corresponding to the vehicle itself according to the vehicle identifier of the vehicle itself.

[0022] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other, and a computer instruction is stored in the memory, and the processor executes the computer instruction to execute the data processing method in the first aspect or any corresponding embodiment thereof.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer instruction is stored, and the computer instruction is used to cause a computer to execute the data processing method in the first aspect or any corresponding embodiment thereof.

[0024] In a fifth aspect, the present invention provides a computer program product, including a computer instruction, and the computer instruction is used to cause a computer to execute the data processing method in the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0025] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0026] Figure 1 is a schematic diagram of a data processing system provided according to an embodiment of the present invention;

[0027] Figure 2 is a flowchart of a data processing method according to an embodiment of the present invention;

[0028] Figure 3Schematic diagram for matching multiple first trajectories and second trajectories according to an embodiment of the present invention;

[0029] Figure 4 Schematic diagram of a data processing method according to an embodiment of the present invention

[0030] Figure 5 Structural block diagram of a data processing device according to an embodiment of the present invention;

[0031] Figure 6 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0032] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] Currently, according to the actual application scenarios of intelligent connected vehicles, a simulation scenario including elements such as roads, traffic signals, obstacles, and other vehicles is constructed. Based on the simulation scenario, an intelligent connected vehicle simulation system for V2X (Vehicle-to-Everything) is constructed. This system can simulate the information interaction process between intelligent connected vehicles and other traffic participants, including vehicle-to-vehicle communication, vehicle-to-infrastructure communication, etc.

[0034] However, although the simulation system can simulate various traffic scenarios and vehicle behaviors, due to factors such as technical level and computing power, it is still unable to fully reproduce the real road environment, resulting in inconsistent performance of intelligent connected vehicles in the actual road environment with the simulation test results, thus affecting their safety and reliability.

[0035] Based on this, the present invention provides a data processing method. By acquiring and analyzing the driving data of traffic participants and the host vehicle, it can immediately reflect the real situation of the current road and dynamically adjust the recognition and tracking of traffic participants. Moreover, by determining the trajectory corresponding to each driving data and finding the target trajectory that matches the host vehicle trajectory, decisions can be made based on actual data instead of relying on the preset model of the simulation system. This method is closer to reality and can more accurately reflect the performance of intelligent connected vehicles in the actual road environment. At the same time, according to the vehicle identification of the host vehicle, the corresponding index coefficient of the host vehicle can be determined, so as to be able to make corresponding strategy adjustments for different index coefficients of the host vehicle.

[0036] Please refer toFigure 1 , Figure 1 is a schematic diagram of a data processing system provided according to an embodiment of the present invention.

[0037] The data processing system includes: a road test ground truth acquisition device, a service data acquisition device, and a data evaluation device.

[0038] The road test ground truth acquisition device includes a traffic participant ground truth function component, a time ground truth component, and a traffic participant graphical component. The road test ground truth acquisition device provides accurate vehicle-related information in a real scenario for the entire data processing system to compare and analyze the accuracy, latency, content integrity, etc. of RSM messages.

[0039] The traffic participant ground truth function component can use the vehicle itself as one of the traffic participants to collect basic vehicle driving information such as the vehicle's heading angle, longitude and latitude, speed, and acceleration through high-precision inertial navigation.

[0040] The time ground truth component can consist of a Network Time Protocol (NTP) time synchronization tool. The time ground truth component is connected to the RSM data acquisition function component and the traffic participant ground truth function component, and stamps the collected data with the same time synchronization source for latency analysis.

[0041] The traffic participant graphical component draws corresponding driving trajectories based on the vehicle driving information in the traffic participant ground truth function component and the RSM data acquisition function component. By analyzing and comparing the trajectories, the ground truth collected is associated with the IDs in the RSM messages. Then, metrics such as latency and position accuracy are evaluated.

[0042] The service data acquisition device includes an RSM data acquisition function component and an RSM data parsing and analysis function component.

[0043] The RSM data acquisition function component uses a spectrum analyzer device as the basic means to collect the RSM data broadcast within the current range. The RSM data parsing and analysis function component parses the RSM messages into readable data according to industry standards for analyzing various evaluation metrics of the data.

[0044] The data evaluation device includes: a metric evaluation function component and an automated report generation component. Among them, the metric evaluation function component can analyze the comprehensive test results and generate an accurate test analysis report. The system calculates and analyzes the C-V2X communication performance metrics based on the data collected by the service data acquisition system. At the same time, it evaluates the overall RSM data quality through comparative analysis with the ground truth data collected by the ground truth system. The automated report generation component generates a report according to the overall RSM data quality.

[0045] According to an embodiment of the present invention, an embodiment of a data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0046] In this embodiment, a data processing method is provided, which can be used in the above data processing system. Figure 2 It is a schematic flowchart of the data processing method according to an embodiment of the present invention, as Figure 2 shown, the process includes the following steps:

[0047] Step S201, obtain the first driving data of traffic participants on the current road and the second driving data of the host vehicle; wherein, the first driving data includes a first identifier.

[0048] The current road can represent the road for testing, that is, the road on which the host vehicle is currently driving. Traffic participants can be used to represent all objects on the current road. Among them, traffic participants can include pedestrians, non-motor vehicles (such as bicycles), and motor vehicles (such as electric vehicles, fuel vehicles, hybrid vehicles, etc.).

[0049] The first driving data can represent the data generated by each traffic participant during the driving process on the current road. The first driving data can be the first identifier, vehicle speed, heading angle, acceleration, longitude and latitude, etc. of the traffic participant, which are not specifically limited here. Among them, the first identifier can be used to represent the unique authentication identifier of each traffic participant.

[0050] It should be noted that the first identifier can correspond to other first driving data of the traffic participant, that is, when the first identifier is determined, all other first driving data corresponding to the first identifier can be determined according to the first identifier.

[0051] The host vehicle can be used to represent the vehicle for testing. The second driving data of the host vehicle can include: the vehicle speed, heading angle, acceleration, longitude and latitude, etc. of the host vehicle on the current road, which are not specifically limited here.

[0052] Specifically, the first driving data of traffic participants on the current road can be broadcast by roadside equipment and then collected by the above RSM data collection function component. The second driving data of the host vehicle can be collected by an inertial navigation device configured on the vehicle.

[0053] In a possible implementation, the antenna can be placed on the roof, the inertial navigation lever arm value can be configured, the receiver installation position can be determined and fixed, and the vehicle can drive briefly until the device is calibrated.

[0054] Step S202: Determine the first trajectory corresponding to each piece of first driving data and the second trajectory corresponding to the second driving data of the current road based on the first driving data and the second driving data.

[0055] After determining the first driving data and the second driving data, the first trajectory can be constructed according to the first driving data, and then the second trajectory can be constructed according to the second driving data. Specifically, the first trajectory is generated by using a trajectory generation algorithm (such as the least squares method, curve fitting, etc.) based on the first driving data. The first trajectory can also be generated by other means, which are not specifically limited here and can be implemented by those skilled in the art.

[0056] As an example, the processed first driving data includes information such as the position (e.g., longitude and latitude), speed, and timestamp of the vehicle. The least squares method is used to fit these data to find an optimal curve to approximately represent the driving trajectory of the vehicle. The obtained first trajectory is a smooth curve that can better reflect the actual driving path of the vehicle.

[0057] As an example, the processed first driving data contains multiple position points of the vehicle. A suitable curve fitting algorithm (such as polynomial fitting, spline curve fitting, etc.) is selected to fit a smooth curve according to the position points. The generated first trajectory is a curve that can better pass through all or most of the position points and reflects the driving path of the vehicle.

[0058] Step S203: Determine the target trajectory corresponding to the second trajectory from multiple first trajectories, and determine the first identifier corresponding to the target trajectory as the self-vehicle identifier of the self-vehicle.

[0059] Since the number of traffic participants on the current road can be multiple, a first trajectory can be determined according to the first driving data of each traffic participant. At the same time, during the driving process of the self-vehicle on the current road, the roadside device will also broadcast, and then it will be collected by the above-mentioned RSM data collection function component. Therefore, the first driving data of the traffic participants on the current road will include the driving data of the self-vehicle, that is, the second trajectory of the self-vehicle will exist in the first trajectories determined according to the first driving data of the traffic participants on the current road. Therefore, to determine the target trajectory corresponding to the second trajectory from multiple first trajectories, since the target trajectory has an identifier, the identifier corresponding to the target trajectory can be used as the self-vehicle identifier of the self-vehicle.

[0060] Please refer to Figure 3 , Figure 3 which is a schematic diagram of matching multiple first trajectories and second trajectories according to an embodiment of the present invention.

[0061] Figure 3Among them, the trajectory map drawn by the traffic participant truth function component can be the second trajectory of the host vehicle, and the other two trajectory maps are respectively the first trajectories corresponding to the first driving data of the traffic parameters of the current road. Among them, the trajectory map corresponding to ptcid1 is the same as the second trajectory, and the trajectory map corresponding to ptcid1 can be used as the target trajectory.

[0062] Step S204: Determine the index coefficient corresponding to the host vehicle according to the host vehicle identifier of the host vehicle.

[0063] The index coefficient corresponding to the host vehicle can be used to indicate the RSM delay, position accuracy, etc. during the driving of the host vehicle on the current road, which is not specifically limited here. As can be seen from the above content, after determining the host vehicle identifier of the host vehicle, the time when the roadside device generates the first driving data corresponding to the host vehicle identifier can be determined, the time when the above RSM data acquisition function component performs acquisition can be determined, and other first driving data of the traffic participant corresponding to the target trajectory can also be determined. Then, according to the time when the roadside device broadcasts the first driving data corresponding to the host vehicle identifier, the time when the above RSM data acquisition function component performs acquisition, and the other first driving data of the traffic participant corresponding to the target trajectory, the index coefficient corresponding to the host vehicle can be determined.

[0064] As an example, first, set up test equipment on an experimental vehicle and perform simple calibration and unified gnss time timing. Drive the test vehicle into the RSU intersection to be measured, and turn on the equipment to capture messages through the pc5 interface; turn on the inertial navigation to record the position, speed, acceleration, and heading angle of the host vehicle; turn on the truth value acquisition device to record the position, speed, acceleration, length, width, height, and heading angle of the surrounding perceived vehicles; the above devices record and save the data and display all the content included in the RSM messages captured by the test equipment after parsing according to the standard file in another window. Export the parsed data into a csv format file, the file recorded by the truth value acquisition device, and the file recorded by the host vehicle inertial navigation together into the automatic generation component to draw the driving trajectories of each traffic participant and the driving trajectory of the host vehicle, and then perform coincidence comparison to find the driving trajectory consistent with the host vehicle trajectory. Determine that the identifier corresponding to this trajectory in the RSM message is the host vehicle identifier. Then, compare the host vehicle inertial navigation information with the information in the RSM message, calculate a series of evaluation indicators to determine the accuracy and availability of the RSM message, collect and analyze the information of multiple intersections, and then generate an automatic report to evaluate the construction situation of the entire demonstration area.

[0065] The data processing method provided in this embodiment can instantly reflect the real situation of the current road and dynamically adjust the recognition and tracking of traffic participants by acquiring and analyzing the driving data of traffic participants and the host vehicle. Moreover, by determining the trajectory corresponding to each driving data and finding the target trajectory that matches the trajectory of the host vehicle, decisions can be made based on actual data instead of relying on the preset model of the simulation system. This method is closer to reality and can more accurately reflect the performance of intelligent connected vehicles in the actual road environment. At the same time, according to the vehicle identification of the host vehicle, the index coefficient corresponding to the host vehicle can be determined, so that corresponding strategy adjustments can be made for different index coefficients of the host vehicle.

[0066] In a possible implementation manner, the above step S204 includes:

[0067] Step a1, determining the generation time of the first driving data corresponding to the vehicle identification of the host vehicle.

[0068] When the host vehicle is driving on the current road, the roadside device will generate the first driving data corresponding to the vehicle identification of the host vehicle during the driving process of the host vehicle. When the RSM data acquisition function component acquires the first driving data, the first driving data will carry the time when the roadside device generates the first driving data corresponding to the vehicle identification of the host vehicle. After determining the vehicle identification, the generation time of the first driving data corresponding to the vehicle identification can be determined from multiple times when the first driving data corresponding to the vehicle identification is generated according to the vehicle identification.

[0069] Step a2, determining the reception time of the first driving data corresponding to the vehicle identification of the host vehicle obtained.

[0070] When the above RSM data acquisition function component acquires the first driving data, the reception time when the RSM data acquisition function component acquires the first driving data is recorded. After determining the vehicle identification, the reception time of the first driving data corresponding to the vehicle identification can be determined from multiple reception times when the RSM data acquisition function component acquires the first driving data according to the vehicle identification.

[0071] Step a3, determining the time difference between the generation time and the reception time.

[0072] After determining the generation time and the reception time corresponding to the host vehicle, the time difference between the generation time and the reception time can be determined according to the generation time and the reception time corresponding to the host vehicle. For example: the generation time is 15:13 seconds, the reception time is 15:16 seconds, and the corresponding time difference is 3 seconds.

[0073] Step a4, determining the type of the host vehicle, the source of the first driving data, and the vehicle position index according to the vehicle identification of the host vehicle.

[0074] When collecting the first driving data through the RSM data collection function component, the source of the first driving data can indicate the first driving data generated by the roadside device, that is, the RSM data. The type of the vehicle itself can be determined according to the corresponding vehicle identification. For example, if the vehicle identification is X1, the type of the vehicle itself can be a motor vehicle; if the vehicle identification is X2, the type of the vehicle itself can be a non-motor vehicle, etc.

[0075] As an example, a relationship table between the vehicle identification and the type of the vehicle itself can be set in advance. After determining the vehicle identification, the type of the vehicle itself can be determined by querying the relationship table.

[0076] The position index of the vehicle itself is used to indicate the accuracy rate of the position of the vehicle itself. Among them, the position index of the vehicle itself can be determined according to the longitude and latitude of the vehicle itself.

[0077] As an example, the RSM data collection function component collects the longitude and latitude in the first driving data, and then the traffic participant truth function component collects the longitude and latitude in the second driving data of the vehicle itself. By comparing the longitude and latitude in the first driving data and the longitude and latitude in the second driving data of the vehicle itself corresponding to the same time, the position index of the vehicle itself can be determined.

[0078] Step a5: Determine the index coefficient corresponding to the vehicle itself according to the type of the vehicle itself, the source of the first driving data, the position index of the vehicle itself, and the time difference.

[0079] After determining the type of the vehicle itself, the source of the first driving data, the position index of the vehicle itself, and the time difference, the index threshold can be determined according to different sources of the first driving data and the type of the vehicle itself, and then the index coefficient corresponding to the vehicle itself can be determined according to the index threshold. The specific content will be described below.

[0080] Specifically, the above step a5 includes:

[0081] Step a51: Determine the index threshold according to the source of the first driving data and the type of the vehicle itself; among them, the index threshold includes: the position index threshold and the difference threshold.

[0082] The index threshold can be used to represent the threshold for whether the position index result and the time difference result meet the requirements. Among them, the index threshold can include: the position index threshold and the difference threshold.

[0083] Each source of the first driving data and the type of the vehicle itself correspond to different index thresholds. For example, when the source of the first driving data is M1 and the type of the vehicle itself is N1, the corresponding index threshold is Y1; when the source of the first driving data is M2 and the type of the vehicle itself is N2, the corresponding index threshold is Y2, etc.

[0084] As an example, a correlation relationship table between the source of the first driving data, the type of the host vehicle, and the index threshold can be pre-configured. When determining the source of the first driving data and the type of the host vehicle, the index threshold can be determined by querying the correlation relationship table.

[0085] Step a52: Determine the host vehicle position index result according to the host vehicle position index and the position index threshold.

[0086] After determining the host vehicle position index and the position index threshold, the host vehicle position index result can be determined according to the ratio between the host vehicle position index and the position index threshold. Among them, the host vehicle position index result can indicate that the host vehicle position index is unqualified or the host vehicle position index is qualified.

[0087] For example: If the host vehicle position index is Q, the position index threshold is W, and Q is less than W, it can indicate that the host vehicle position index is unqualified; conversely, if Q is greater than W, it can indicate that the host vehicle position index is qualified.

[0088] Step a53: Determine the time difference result according to the time difference and the difference threshold.

[0089] After determining the time difference and the difference threshold, the time difference result can be determined according to the ratio between the time difference and the difference threshold. Among them, the time difference result can indicate that the time difference is unqualified or the time difference is qualified.

[0090] For example: If the time difference is t, the difference threshold is T, and t is less than T, it can indicate that the time difference is qualified, that is, the RSM delay is small; conversely, if t is greater than T, it can indicate that the time difference is unqualified.

[0091] Step a54: Determine the index coefficient corresponding to the host vehicle according to the host vehicle position index result, the time difference result, the source of the first driving data, and the type of the host vehicle.

[0092] The host vehicle position index result, the time difference result, the source of the first driving data, and the type of the host vehicle can be used as the index coefficient corresponding to the host vehicle. For example: The source of the first driving data is M3, the type of the host vehicle is N3, the time difference result is that the time difference is qualified, and the host vehicle position index result is that the host vehicle position index is unqualified.

[0093] In a possible implementation manner, after determining the index coefficient corresponding to the host vehicle, the index coefficients of multiple roads can be determined, and then an evaluation report can be generated according to the index coefficients of the multiple roads.

[0094] The data processing method provided in this embodiment can evaluate the data transmission efficiency and latency by determining the generation time and reception time of the first driving data and calculating the time difference between them, timely detect problems in data transmission, and thus improve the timeliness of data processing. At the same time, by comprehensively considering factors such as the type of the vehicle itself, the source of driving data, position indicators, and time difference, more refined index coefficients can be determined. These index coefficients can provide more comprehensive information support for evaluating aspects such as the driving performance, safety, and data transmission quality of the vehicle itself.

[0095] In a possible implementation manner, the step of obtaining the second driving data of the vehicle itself includes:

[0096] Step b1, obtain a preset trajectory; where the preset trajectory is used to indicate the driving route of the vehicle itself on the current road.

[0097] The preset trajectory can be used to indicate the driving route of the vehicle itself on the current road. Among them, the preset trajectory can be an arc or a straight line, etc., and no specific limitation is made here.

[0098] Step b2, when the vehicle itself travels according to the preset trajectory, obtain the second driving data of the vehicle itself; where the second driving data of the vehicle itself includes: driving speed, driving acceleration, time, longitude and latitude, and heading angle.

[0099] During the process of the vehicle itself traveling according to the preset trajectory, the second driving data of the vehicle itself; where the second driving data of the vehicle itself may include: driving speed, driving acceleration, time, longitude and latitude, and heading angle.

[0100] In the data processing method provided in this embodiment, since the corresponding trajectories of traffic participants are all relatively regular trajectories during the driving process on the current road, and the vehicle itself travels on the current road according to the preset trajectory, the trajectory obtained by the vehicle itself can be easily determined from multiple traffic participant trajectories.

[0101] In a possible implementation manner, the first driving data includes: first longitude and latitude, and the second driving data includes: second longitude and latitude; where the above step 202 includes:

[0102] Step c1, obtain the first longitude and latitude and the second longitude and latitude collected at each preset time point.

[0103] The first longitude and latitude can be used to represent the position of the traffic participant on the current road corresponding to a preset time point. The second longitude and latitude are used to represent the position of the host vehicle corresponding to the preset time point. Specifically, during the process of the host vehicle driving on the current road, the acquisition time can be preset, and the first driving data (such as the first longitude and latitude) and the second driving data (the second longitude and latitude) can be collected according to the preset acquisition time. For example, data is collected every n1 seconds, every n2 seconds, etc.

[0104] Step c2: Perform coordinate transformation on the first longitude and latitude and the second longitude and latitude respectively to determine the first planar coordinate corresponding to the first longitude and latitude and the second planar coordinate corresponding to the second longitude and latitude.

[0105] In order to more accurately determine the position of the traffic participant and the position of the host vehicle, it is necessary to perform coordinate transformation on the first longitude and latitude and the second longitude and latitude respectively to determine the first planar coordinate corresponding to the first longitude and latitude and the second planar coordinate corresponding to the second longitude and latitude.

[0106] As an example, use the geographic information processing library in the programming language, such as the pyproj library in Python, GeoTools in Java, SharpMap in C#, etc. Call the online API service, such as Google Maps API, Bing Maps API, etc., to implement the coordinate transformation function.

[0107] As an example, calculate the first planar coordinate corresponding to the first longitude and latitude and the second planar coordinate corresponding to the second longitude and latitude according to the Mercator projection formula.

[0108] As an example, in ArcGIS, the "Project Tool" can be used to obtain the first planar coordinate corresponding to the first longitude and latitude and the second planar coordinate corresponding to the second longitude and latitude. In QGIS, functions such as "Raster" > "Projection" or "Vector" > "Geometric Tools" > "Transformation" can be used for coordinate transformation to obtain the first planar coordinate corresponding to the first longitude and latitude and the second planar coordinate corresponding to the second longitude and latitude.

[0109] Step c3: Determine the first position corresponding to each preset time point according to the first planar coordinate.

[0110] Step c4: Determine the second position corresponding to each preset time point according to the second planar coordinate.

[0111] After determining the first planar coordinate and the second planar coordinate corresponding to each preset time point, the positions of the host vehicle and the traffic participant at each preset time point can be determined according to the first planar coordinate system (for example, a two-dimensional Cartesian coordinate system). These preset time points can be equally spaced or set according to a certain specific condition.

[0112] As an example, the vehicle moves on a two-dimensional plane, and the preset time points are t1, t2, t3... tn. For each time point, there is a corresponding coordinate (x1, y1), (x2, y2), (x3, y3)... (xn, yn), and these coordinates represent the position of the vehicle at the corresponding time point.

[0113] Step c5: Connect the first positions corresponding to each preset time point to form a first trajectory.

[0114] According to the determined first positions, the first positions can be connected in the ascending order of the numerical values of the abscissas corresponding to the first positions, or the first positions can be connected in other ways to form a first trajectory. Specific limitations are not made here, and it can be implemented by those skilled in the art.

[0115] As an example, connecting the points (x1, y1), (x2, y2), (x3, y3)... (xn, yn) in sequence forms the movement trajectory of the vehicle in the first plane coordinate system.

[0116] Step c6: Connect the second positions corresponding to each preset time point to form a second trajectory.

[0117] According to the determined second positions, the second positions can be connected in the ascending order of the numerical values of the abscissas corresponding to the second positions, or the second positions can be connected in other ways to form a second trajectory. Specific limitations are not made here, and it can be implemented by those skilled in the art.

[0118] The data processing method provided in this embodiment can eliminate the influence of the earth's curvature on position calculation by converting longitude and latitude into plane coordinates, making the position determination more accurate.

[0119] At the same time, by obtaining the longitude and latitude data collected at each preset time point, the continuity of the trajectory can be ensured. The position corresponding to each time point is calculated based on accurate longitude and latitude data, which helps to form a continuous and accurate trajectory. Moreover, connecting the positions corresponding to each preset time point can form an intuitive trajectory map for trajectory analysis and visual display.

[0120] In a possible implementation manner, determining the target trajectory corresponding to the second trajectory from multiple first trajectories in step 203 includes:

[0121] Step d1: Obtain the first timestamp corresponding to each first trajectory among multiple first trajectories and the second timestamp corresponding to the second trajectory.

[0122] Step d2: Determine the target timestamp that is the same as the second timestamp from the first timestamps.

[0123] The traffic participant truth value acquisition function component collects, records, and saves second driving data with a second timestamp. The RSM data acquisition function component can collect first driving data in real time. Before the host vehicle enters the current road, the RSM data acquisition function component has collected multiple groups of first driving data of traffic participants, and the collection of the first driving data of each traffic participant is accompanied by a first timestamp. After the traffic participant truth value acquisition function component collects the second driving data, it is necessary to determine a target timestamp identical to the second timestamp from the first timestamps, that is, the target timestamp when the traffic participant truth value acquisition function component and the RSM data acquisition function component collect data simultaneously.

[0124] Step d3: Determine a target trajectory identical to the first trajectory from the second trajectory corresponding to the target timestamp.

[0125] After determining the target timestamp, the first trajectory corresponding to the target timestamp can be determined according to the target timestamp, and then the target trajectory identical to the second trajectory is determined from the first trajectory.

[0126] The data processing method provided in this embodiment first determines a target timestamp identical to the second timestamp through timestamps, which can exclude the influence of trajectories at other different times, so as to more accurately determine the target trajectory identical to the second trajectory.

[0127] Please refer to Figure 4 , Figure 4 which is a schematic diagram of the data processing method provided according to the embodiment of the present invention.

[0128] The road test device can receive v2x messages broadcast by traffic participants and sensor perception information sent by sensors such as cameras and lidar. The road test device generates and broadcasts RSM messages according to the broadcast v2x messages and perception information. The RSM data acquisition function component receives the broadcast RSM messages and parses the RSM messages through the RSM data parsing and analysis function component to obtain the parsed messages.

[0129] The host vehicle is equipped with a data acquisition device and an inertial navigation system. The antenna is placed on the roof, the inertial navigation lever arm value is configured, the receiver installation position is determined and fixed, the GNSS time is sent to the time truth component through the antenna, and then the time truth component is connected to the RSM data acquisition function component and the traffic participant truth value function component, and the collected data is marked with the time truth (timestamp) using the same time timing source. The traffic participant graphical component draws corresponding driving trajectories according to the vehicle driving information in the traffic participant truth value function component and the RSM data acquisition function component. The collected truth values are associated with the identifiers in the RSM messages through trajectory analysis and comparison.

[0130] After determining the identifier, the message corresponding to the identifier is determined from the parsed message, and then the message corresponding to the identifier is input into the index evaluation function component. The index evaluation function component calculates indexes such as end-to-end delay (i.e., the above time difference), traffic participant type, information source, and position accuracy. The automated report generation component generates a report according to the indexes.

[0131] In this embodiment, a data processing device is further provided. The device is used to implement the above embodiment and the preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" may be a combination of software and / or hardware that can implement a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0132] This embodiment provides a data processing device, as Figure 5 shown, including:

[0133] An acquisition module 501, configured to acquire first driving data of traffic participants on the current road and second driving data of the vehicle itself; wherein, the first driving data includes a first identifier; a first determination module 502, configured to determine a first trajectory corresponding to each piece of first driving data on the current road and a second trajectory corresponding to the second driving data according to the first driving data and the second driving data; a second determination module 503, configured to determine a target trajectory corresponding to the second trajectory from multiple first trajectories, and determine the vehicle identifier of the vehicle itself according to the first identifier corresponding to the target trajectory; a third determination module 504, configured to determine an index coefficient corresponding to the vehicle itself according to the vehicle identifier of the vehicle itself.

[0134] In a possible implementation manner, the third determination module 504 includes: a first determination unit, configured to determine the generation time of the first driving data corresponding to the vehicle identifier; a second determination unit, configured to determine the reception time of the first driving data corresponding to the vehicle identifier obtained; a third determination unit, configured to determine the time difference between the generation time and the reception time; a fourth determination unit, configured to determine the type of the vehicle itself, the source of the first driving data, and the vehicle position index according to the vehicle identifier; a fifth determination unit, configured to determine the index coefficient corresponding to the vehicle itself according to the type of the vehicle itself, the source of the first driving data, the vehicle position index, and the time difference.

[0135] In a possible implementation, the fifth determination unit includes: a first determination subunit, configured to determine an index threshold according to the source of the first driving data and the type of the host vehicle; wherein, the index threshold includes: a position index threshold and a difference threshold; a second determination subunit, configured to determine a host vehicle position index result according to the host vehicle position index and the position index threshold; a third determination subunit, configured to determine a time difference result according to the time difference and the difference threshold; a fourth determination subunit, configured to determine an index coefficient corresponding to the host vehicle according to the host vehicle position index result, the time difference result, the source of the first driving data, and the type of the host vehicle.

[0136] In a possible implementation, the acquisition module 501 includes: a first acquisition subunit, configured to acquire a preset trajectory; wherein, the preset trajectory is used to indicate the driving route of the host vehicle on the current road; a second acquisition subunit, configured to acquire second driving data of the host vehicle when the host vehicle travels according to the preset trajectory; wherein, the second driving data of the host vehicle includes: driving speed, driving acceleration, time, longitude and latitude, and heading angle.

[0137] In a possible implementation, the first driving data includes: first longitude and latitude, and the second driving data includes: second longitude and latitude; wherein, the first determination module 502 includes: a timestamp acquisition module, configured to acquire a first timestamp corresponding to each of a plurality of first trajectories and a second timestamp corresponding to a second trajectory; a timestamp determination module, configured to determine a target timestamp identical to the second timestamp from the plurality of first timestamps; a target trajectory determination module, configured to determine a target trajectory identical to the second trajectory from the first trajectories corresponding to the target timestamp.

[0138] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0139] The data processing device in this embodiment is presented in the form of functional units. Here, the functional units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0140] The embodiment of the present invention further provides a computer device having the above Figure 5 shown data processing device.

[0141] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 6 In this, one processor 10 is taken as an example.

[0142] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0143] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0144] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0145] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0146] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0147] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0148] A part of the present invention can be applied as a computer program product, for example, computer program instructions. When executed by a computer, through the operation of the computer, the methods and / or technical solutions according to the present invention can be called or provided. Those skilled in the art should be able to understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0149] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire first driving data of a traffic participant on the current road and second driving data of the vehicle; wherein the first driving data includes a first identifier; Determine, according to the first driving data and the second driving data, a first trajectory corresponding to each first driving data and a second trajectory corresponding to each second driving data of a current road; Determine a target track corresponding to the second track from the plurality of first tracks, and determine a first identifier corresponding to the target track as the vehicle identifier of the vehicle; According to the vehicle identifier of the vehicle, an index coefficient corresponding to the vehicle is determined.

2. The data processing method according to claim 1, characterized in that: The step of determining the index coefficient corresponding to the vehicle according to the vehicle identification of the vehicle includes: Determine the generation time of the first driving data corresponding to the vehicle identifier; Determine the reception time of the first driving data corresponding to the vehicle identifier; Determine a time difference between the generation time and the reception time; Determining the type of the vehicle, the source of the first driving data, and the vehicle position indicator according to the vehicle identifier; An index coefficient corresponding to the own vehicle is determined according to the type of the own vehicle, the source of the first driving data, the own vehicle position index and the time difference.

3. The data processing method according to claim 2, characterized in that: The step of determining the index coefficient corresponding to the vehicle according to the type of the vehicle, the source of the first driving data, the vehicle position index, and the time difference includes: Determine an index threshold according to the source of the first driving data and the type of the vehicle; wherein the index threshold includes: a position index threshold and a difference threshold; Determining a vehicle position index result according to the vehicle position index and the position index threshold; Determine a time difference result according to the time difference and the difference threshold; An index coefficient corresponding to the own vehicle is determined according to the own vehicle position index result, the time difference result, the source of the first driving data, and the type of the own vehicle.

4. The data processing method according to claim 1, characterized in that: The second driving data of the vehicle is obtained, including: Obtaining a preset trajectory; wherein the preset trajectory is used to indicate the driving route of the vehicle on the current road; When the vehicle travels according to the preset trajectory, the second travel data of the vehicle is obtained; wherein the second travel data of the vehicle includes: travel speed, travel acceleration, time, longitude and latitude, and heading angle.

5. The data processing method according to claim 1, characterized in that: The first driving data includes: a first longitude and latitude, and the second driving data includes: a second longitude and latitude; wherein, determining a first trajectory corresponding to each first driving data and a second trajectory corresponding to each second driving data of the current road according to the first driving data and the second driving data includes: Obtain the first longitude and longitude and the second longitude and longitude collected at each preset time point; Performing coordinate conversion on the first longitude and latitude and the second longitude and latitude respectively to determine the first plane coordinates corresponding to the first longitude and latitude and the second plane coordinates corresponding to the second longitude and latitude; Determine a first position corresponding to each preset time point according to the first plane coordinates; Determine a second position corresponding to each preset time point according to the second plane coordinates; Connecting the first positions corresponding to each preset time point to form a first trajectory; The second positions corresponding to each preset time point are connected to form a second track.

6. The data processing method according to claim 1, characterized in that: Determining a target trajectory corresponding to the second trajectory from a plurality of the first trajectories includes: Obtaining a first timestamp corresponding to each first track and a second timestamp corresponding to each second track in the plurality of first tracks; Determine a target timestamp that is the same as the second timestamp from among the plurality of the first timestamps; A target trajectory identical to the second trajectory is determined from the first trajectory corresponding to the target timestamp.

7. A data processing device, characterized in that: The device comprises: An acquisition module, used to acquire first driving data of a traffic participant on the current road and second driving data of the vehicle; wherein the first driving data includes a first identifier; A first determining module, configured to determine a first trajectory corresponding to each first driving data and a second trajectory corresponding to each second driving data of a current road according to the first driving data and the second driving data; a second determination module, configured to determine a target track corresponding to the second track from the plurality of first tracks, and determine a vehicle identifier of the vehicle according to a first identifier corresponding to the target track; The third determination module is used to determine the index coefficient corresponding to the own vehicle according to the own vehicle identification of the own vehicle.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the data processing method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the data processing method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the data processing method according to any one of claims 1 to 6.

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