Vehicle driving data processing system, method and device, medium and electronic equipment

Abstract

The invention discloses a vehicle driving data processing system, method and device, a medium and electronic equipment, the system comprises a client and a server, and the server comprises a container cluster management module, a task scheduling module and a basic code library; the client is used for outputting a task configuration interface for selecting tasks, receiving task configuration information and a to-be-processed original driving data set, and sending the task configuration information and the original driving data set to the server; the task configuration information comprises at least one task name selected based on the task configuration interface and a task execution sequence set when a plurality of task names are selected; the container cluster management module is used for deploying and managing a container cluster of the system; the basic code library is used for providing basic codes required for executing each task; and the task scheduling module is used for scheduling the target task according to the task configuration information, and processing the original driving data set by executing the basic code and the task code of the target task to obtain a target driving data set.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and more specifically, to a vehicle driving data processing system, method, device, medium and electronic device. Background Art

[0002] AI (Artificial Intelligence) is one of the key technologies that are indispensable for realizing autonomous driving. In order to train the autonomous driving model, it is necessary to collect a large amount of raw driving data from the vehicle and obtain the driving data required for training the autonomous driving model from these raw driving data. However, autonomous driving involves many scenarios, and the processing logic of the raw driving data may be different due to different road conditions and different data sources. When each user (that is, the technician who obtains the data required for training the autonomous driving model from the raw data) encounters a new scenario, he needs to rewrite a code, which greatly affects the efficiency of obtaining the required driving data. Summary of the invention

[0003] The present application provides a vehicle driving data processing system, method, device, medium and electronic device, which can provide a system that can freely perform tasks and task execution order without the need for users to write code, thereby improving the efficiency of users in obtaining target driving data sets.

[0004] The specific technical solutions are as follows:

[0005] In a first aspect, an embodiment of the present application provides a vehicle driving data processing system, the system comprising a client and a server, the server comprising a container cluster management module, a task scheduling module and a basic code library;

[0006] The client is used to output a task configuration interface for selecting tasks, receive task configuration information set based on the task configuration interface and receive an original driving data set to be processed, and send the task configuration information and the original driving data set to the server, wherein the task configuration information includes at least one task name selected based on the task configuration interface and a task execution order set when multiple task names are selected;

[0007] The container cluster management module is used to deploy and manage the container cluster of the system, wherein each of the tasks is executed on a container, and each of the tasks is an algorithm for processing driving data;

[0008] The basic code library is used to provide the basic code required to perform each task;

[0009] The task scheduling module is used to schedule the target task according to the task configuration information, and process the original driving data set by executing the basic code of the target task and the task code of the target task to obtain the target driving data set, wherein the task code depends on the basic code when executing.

[0010] It can be seen from the above scheme that the embodiment of the present application can provide a system for freely selecting tasks and task execution order. After the user sets the task configuration information on the client side of the system according to his own needs, the task scheduling module in the server side of the system can schedule the target task according to the task configuration information, and the original driving data set is processed by executing the basic code of the target task and the task code of the target task to obtain the target driving data set. The whole process does not require manual code writing, which not only saves labor costs but also improves the efficiency of users in obtaining the target driving data set.

[0011] In a first possible implementation manner of the first aspect, the task scheduling module is used to, when the task configuration information includes multiple task names, filter out a target task code set from a task code library according to the multiple task names in the task configuration information and the task execution order, and process the original driving data set by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library to obtain the target driving data set.

[0012] In a second possible implementation manner of the first aspect, the task code library includes a task code of at least one of the following tasks: a first task, a second task, and a third task;

[0013] The first task is used to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain a valid original driving data set;

[0014] The second task is used to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain the high-precision original driving data set;

[0015] The third task is used to convert the high-precision original driving data set into the target driving data set that meets the training requirements of the autonomous driving model.

[0016] It can be seen from the above scheme that the tasks corresponding to the task code library provided in the embodiment of the present application include at least the following tasks: a task for screening valid data, a task for converting low-precision positioning into high-precision positioning and updating high-precision maps, and a task for converting a high-precision original driving data set into a target driving data set that meets the training requirements of the autonomous driving model. Users can select the required tasks according to their own needs.

[0017] In a third possible implementation manner of the first aspect, the system further includes:

[0018] Online code calling library, including callable online task codes;

[0019] The task scheduling module is used to replace the corresponding task code in the task code library with the online task code of the target task in the online code calling library and add it to the target task code set when the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code calling library.

[0020] It can be seen from the above scheme that the embodiment of the present application adds an online code call library to the system. When the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code call library, the online task code of the target task in the online code call library replaces the corresponding task code in the task code library and adds it to the target task code set. This can avoid the problem that the online autonomous driving model cannot be accurately updated or cannot be updated due to the inconsistency between the online task code and the offline task code (i.e., the corresponding task code in the task code library).

[0021] In a fourth possible implementation manner of the first aspect, the task configuration information further includes a user-defined task script.

[0022] It can be seen from the above scheme that the embodiment of the present application can also provide the function of user-defined task scripts to achieve personalized processing of the original driving data set.

[0023] In a second aspect, an embodiment of the present application provides a method for processing vehicle driving data, the method comprising:

[0024] Receiving task configuration information and an original driving data set to be processed sent by a client, wherein the task configuration information includes at least one task name selected based on a task configuration interface output by the client and a task execution order set when multiple task names are selected, and each task is an algorithm for processing driving data;

[0025] The target task is scheduled according to the task configuration information, and the original driving data set is processed by executing a basic code of the target task and a task code of the target task to obtain a target driving data set, wherein the task code depends on the basic code when executing.

[0026] In a first possible implementation manner of the second aspect, scheduling a target task according to the task configuration information, and processing the original driving data set by executing a basic code of the target task and a task code of the target task to obtain a target driving data set includes:

[0027] When the task configuration information includes a plurality of task names, according to the plurality of task names and the task execution order in the task configuration information, a target task code set is screened out from a task code library, and a corresponding basic code is screened out from a basic code library;

[0028] The target driving data set is obtained by processing the original driving data set by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library.

[0029] In a second possible implementation manner of the second aspect, the task code library includes a task code of at least one of the following tasks: a first task, a second task, and a third task;

[0030] The first task is used to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain the valid original driving data set;

[0031] The second task is used to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain the high-precision original driving data set;

[0032] The third task is used to convert the high-precision original driving data set into the target driving data set that meets the training requirements of the autonomous driving model.

[0033] In a third possible implementation manner of the second aspect, the method further includes:

[0034] When the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code calling library, the online task code of the target task in the online code calling library replaces the corresponding task code in the task code library and is added to the target task code set.

[0035] In a fourth possible implementation manner of the second aspect, the task configuration information further includes a user-defined task script.

[0036] It can be seen from the above scheme that the embodiment of the present application can provide a system for freely selecting tasks and task execution order. After the user sets the task configuration information on the client side of the system according to his own needs, the task scheduling module in the server side of the system can schedule the target task according to the task configuration information, and the original driving data set is processed by executing the basic code of the target task and the task code of the target task to obtain the target driving data set. The whole process does not require manual code writing, which not only saves labor costs but also improves the efficiency of users in obtaining the target driving data set.

[0037] In a third aspect, an embodiment of the present application provides a device for processing vehicle driving data, the device comprising:

[0038] A receiving unit, configured to receive task configuration information and an original driving data set to be processed sent by a client, wherein the task configuration information includes at least one task name selected based on a task configuration interface output by the client and a task execution order set when multiple task names are selected, and each task is an algorithm for processing driving data;

[0039] A task execution unit is used to schedule a target task according to the task configuration information, and process the original driving data set by executing a basic code of the target task and a task code of the target task to obtain a target driving data set, wherein the task code depends on the basic code when executing.

[0040] In a first possible implementation manner of the third aspect, the task execution unit includes:

[0041] A screening module, for screening out a target task code set from a task code library and a corresponding basic code from a basic code library according to the multiple task names and the task execution order in the task configuration information when the task configuration information includes multiple task names;

[0042] An execution module is used to process the original driving data set by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library to obtain the target driving data set.

[0043] In a second possible implementation manner of the third aspect, the task code library includes a task code of at least one of the following tasks: a first task, a second task, and a third task;

[0044] The first task is used to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain the valid original driving data set;

[0045] The second task is used to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain the high-precision original driving data set;

[0046] The third task is used to convert the high-precision original driving data set into the target driving data set that meets the training requirements of the autonomous driving model.

[0047] In a third possible implementation method of the third aspect, the task execution unit is also used to replace the corresponding task code in the task code library with the online task code of the target task in the online code calling library, and add the online task code of the target task in the online code calling library to the target task code set when the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code calling library.

[0048] In a fourth possible implementation manner of the third aspect, the task configuration information also includes a user-defined task script.

[0049] It can be seen from the above scheme that the embodiment of the present application can provide a system for freely selecting tasks and task execution order. After the user sets the task configuration information on the client side of the system according to his own needs, the task scheduling module in the server side of the system can schedule the target task according to the task configuration information, and the original driving data set is processed by executing the basic code of the target task and the task code of the target task to obtain the target driving data set. The whole process does not require manual code writing, which not only saves labor costs but also improves the efficiency of users in obtaining the target driving data set.

[0050] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in or any possible implementation of the second aspect.

[0051] In a fifth aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0052] one or more processors;

[0053] The processor is coupled to a storage device, and the storage device is used to store one or more programs;

[0054] When one or more programs are executed by one or more processors, the electronic device implements the method described in any possible implementation manner of the second aspect.

[0055] In a sixth aspect, an embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed on a computer or a processor, the computer or the processor executes the method described in any possible implementation of the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art description are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work.

[0057] Figure 1 A schematic diagram of the structure of a vehicle driving data processing system provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of the structure of another vehicle driving data processing system provided in an embodiment of the present application;

[0059] Figure 3 A flowchart of a method for processing vehicle driving data provided in an embodiment of the present application;

[0060] Figure 4 A block diagram of a vehicle driving data processing device provided in an embodiment of the present application;

[0061] Figure 5 A schematic diagram of the structure of an electronic device or computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The terms "including" and "having" in the embodiments of the present application and the accompanying drawings and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0064] Figure 1 It is a structural diagram of a vehicle driving data processing system, the system includes a client 110 and a server 120, the server 120 includes a container cluster management module 121, a task scheduling module 122 and a basic code library 123;

[0065] The client 110 is used to output a task configuration interface for selecting tasks, receive task configuration information set based on the task configuration interface and receive an original driving data set to be processed, and send the task configuration information and the original driving data set to the server 120, wherein the task configuration information includes at least one task name selected based on the task configuration interface and a task execution order set when multiple task names are selected;

[0066] A container cluster management module 121, used to deploy and manage a container cluster of the system, wherein each task is executed on a container, and each task is an algorithm for processing driving data;

[0067] A basic code library 123 is used to provide basic codes required to perform each task;

[0068] The task scheduling module 122 is used to schedule the target task according to the task configuration information, and process the original driving data set by executing the basic code of the target task and the task code of the target task to obtain the target driving data set, wherein the task code depends on the basic code when executing.

[0069] In an embodiment of the present application, after the user logs in to the vehicle driving data processing system through the client 110, he can enter the task configuration interface, which contains the task names of all tasks pre-provided by the system. The user can select the task name of the required task, and when multiple task names are selected, the execution order of the tasks corresponding to the multiple task names can also be set. The task configuration interface can also set the import function of the original driving data set for the user to import the original driving data set to be processed. Of course, the import function of the original driving data set can also be set in other interfaces. Among them, the original driving data set includes data collected by multiple acquisition devices on the vehicle, such as road environment images collected by the image collector, radar point clouds collected by the laser radar, positioning results of the positioning device, vehicle speed measured by the speed sensor, map information used during driving, etc.

[0070] The container cluster management module 121 can be a kubernetes cluster management system, which supports automated deployment, large-scale scalability, and application container management. The embodiment of the present application can deploy multiple containers in each server of the server cluster, each container performs a task, or multiple containers perform a task, or each container performs multiple tasks.

[0071] The basic code is also the historical code. In order to facilitate system updates and reduce the manual writing costs of updates, updates can be made based on the historical code in the old version of the system without rewriting all the code. Specifically, the code required for the new version of the system can be obtained from the historical code of the old version of the system, and then new code that can be stored independently can be written based on the code, but the new code depends on the historical code when it is executed. The historical code here can be called the basic code, and the new code can be called the task code, that is, the basic code + task code = a complete code for a task. The basic code of each task may be the same or different.

[0072] The target driving data set may be a training set for training an autonomous driving model. An autonomous driving model is a machine learning model, including a decision-making planning model, a landmark perception model that obtains map information through perception, and other models required in the autonomous driving process.

[0073] The vehicle driving data processing system provided in the embodiment of the present application can provide a system for freely selecting tasks and task execution order. After the user sets the task configuration information on the client side of the system according to his own needs, the task scheduling module in the server side of the system can schedule the target task according to the task configuration information, and the original driving data set is processed by executing the basic code of the target task and the task code of the target task to obtain the target driving data set. The entire process does not require manual code writing, which not only saves labor costs but also improves the efficiency of users in obtaining the target driving data set.

[0074] In one embodiment, Figure 2 As shown, the task scheduling module 122 is used to filter out a target task code set 1222 from a task code library 1221 according to the multiple task names and the task execution order in the task configuration information when the task configuration information includes multiple task names, and to process the original driving data set by executing the task code of the target task in the target task code set 1222 and the corresponding basic code in the basic code library 123 to obtain the target driving data set.

[0075] The task scheduling module 122 includes a task code library 1221, which includes task codes for various tasks. After obtaining the task configuration information sent by the client 110, the task codes corresponding to the task names with a higher ranking and the task codes corresponding to the task names with a lower ranking can be obtained from the task code library 1221 in turn according to the multiple task names and the task execution order in the task configuration information, and finally a target task code set 1222 is obtained and saved. Among them, the task corresponding to each task name in the task configuration information can be called a target task. For the basic code of the target task, the basic code in the basic code library 123 can be directly called through the call information recorded in the task code of the target task when executing the task code of the target task, or the basic code of the target task can be screened out from the basic code library 123 in advance according to the task configuration information.

[0076] In one implementation, the task code library 1221 includes task codes for at least one of the following tasks: a first task, a second task, and a third task;

[0077] The first task is to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain a valid original driving data set;

[0078] The second task is to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain a high-precision original driving data set;

[0079] The third task is to convert the high-precision original driving dataset into a target driving dataset that meets the training requirements of the autonomous driving model.

[0080] Among them, according to the acquisition device to which each frame of original driving data in the original driving data set belongs, the method for judging whether the original driving data is valid data includes: judging whether there is data missing in the original driving data collected by the acquisition device within a continuous period of time according to the data format, data timestamp and other information of the data collected by the acquisition device; if there is data missing, the original driving data collected by the acquisition device during this period is determined to be invalid data; if there is no data missing, the original driving data collected by the acquisition device during this period is determined to be valid data.

[0081] After obtaining a valid original driving data set, if it is determined that a high-precision positioning algorithm was not used during the collection of the valid original driving data set, the low-precision positioning result in the valid original driving data set can be modified to a high-precision positioning result. Since the positioning position in the high-precision positioning result is different from the positioning position in the low-precision positioning result, it is also necessary to modify the corresponding high-precision map based on the obtained high-precision positioning result to obtain a high-precision original driving data set. For example, the low-precision positioning result is a certain position where the vehicle crosses the line, but in fact the vehicle does not cross the line. The low-precision positioning result can be repaired to obtain a high-precision positioning result so that the vehicle is in the correct position. Low-precision positioning includes navigation positioning. The specific implementation method of modifying the low-precision positioning result to the high-precision positioning result can refer to the existing related technology, which will not be repeated here.

[0082] The third task may also include multiple subtasks, each of which is used to screen whether each frame of raw driving data in the high-precision raw driving data set meets the target driving data set required for the autonomous driving model training, or to convert each frame of raw driving data in the high-precision raw driving data set into a target driving data set that meets the autonomous driving model training format. For example, one subtask may include determining whether the vehicle crosses the line, and if so, filtering out the corresponding raw driving data, and another subtask may include converting the high-precision map into a map that meets the autonomous driving model training format, such as representing each lane line in the high-precision map with N shape point coordinates on the lane line, where N is a positive integer, such as 10.

[0083] It can be seen from the above scheme that the tasks corresponding to the task code library provided in the embodiment of the present application include at least the following tasks: a task for screening valid data, a task for converting low-precision positioning into high-precision positioning and updating high-precision maps, and a task for converting a high-precision original driving data set into a target driving data set that meets the training requirements of the autonomous driving model. Users can select the required tasks according to their own needs.

[0084] In one implementation, whether a real car uses an autonomous driving model for autonomous driving (which can be called online) or trains an autonomous driving model on a training device (which can be called offline), the original driving data set needs to be converted into a target driving data set. However, since online and offline may be maintained by different users, the coding languages ​​used by the two may also be different. Therefore, although the processing logic of the two is the same, the input data of the online autonomous driving model and the input data of the offline autonomous driving model may be inconsistent in format. For example, the input data of the online autonomous driving model uses 12 points to represent a lane line, and the input data of the offline autonomous driving model uses 10 points to represent a lane line. In this case, it may not be possible to better optimize the online autonomous driving model. In order to solve this technical problem, Figure 2 As shown, the system also includes:

[0085] An online code calling library 124 includes callable online task codes;

[0086] The task scheduling module 122 is used to replace the corresponding task code in the task code library 1221 with the online task code of the target task in the online code call library 124, and add it to the target task code set 1222 when the task code of the target task obtained from the task code library 1221 is different from the online task code of the target task in the online code call library 124.

[0087] The embodiment of the present application can obtain callable online task codes by processing online task codes, and store the callable online task codes of multiple tasks in the online code calling library 124. When the task code of the target task obtained from the task code library 1221 is different from the online task code of the target task in the online code calling library 124, the online task code of the target task in the online code calling library replaces the corresponding task code in the task code library 1221 and is added to the target task code set 1222 to realize the calling and execution of the online task code.

[0088] The embodiment of the present application adds an online code call library to the system. When the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code call library, the online task code of the target task in the online code call library replaces the corresponding task code in the task code library and is added to the target task code set. This can avoid the problem that the online autonomous driving model cannot be accurately updated or cannot be updated due to the inconsistency between the online task code and the offline task code (i.e., the corresponding task code in the task code library).

[0089] In one embodiment, the task configuration information also includes a user-defined task script. Figure 2As shown, when the task configuration information received by the system also includes a user-defined task script, the user-defined task script 1223 can be stored in the task scheduling module 122 so that the task scheduling module 122 can call it to achieve personalized processing of the original driving data set.

[0090] Based on the above system embodiment, another embodiment of the present application provides a method for processing vehicle driving data, which can be applied to the server, such as Figure 3 As shown, the method includes:

[0091] S210: Receive task configuration information and the original driving data set to be processed sent by the client.

[0092] The task configuration information includes at least one task name selected based on the task configuration interface output by the client and the task execution order set when multiple task names are selected. The task configuration information also includes a user-defined task script. Each task is an algorithm for processing driving data.

[0093] After the user logs in to the vehicle driving data processing system through the client, he can enter the task configuration interface, which contains the task names of all tasks pre-provided by the system. The user can select the task name of the required task, and when multiple task names are selected, the execution order of the tasks corresponding to the multiple task names can also be set. The task configuration interface can also set the import function of the original driving data set for the user to import the original driving data set to be processed. Of course, the import function of the original driving data set can also be set in other interfaces. Among them, the original driving data set includes data collected by multiple acquisition devices on the vehicle, such as road environment images collected by the image collector, radar point clouds collected by the laser radar, positioning results of the positioning device, vehicle speed measured by the speed sensor, map information used during driving, etc.

[0094] S220: Scheduling the target task according to the task configuration information, and processing the original driving data set by executing the basic code of the target task and the task code of the target task to obtain the target driving data set.

[0095] The task code depends on the base code when it is executed, that is, the complete code of a task consists of the base code and the task code. The base code of each task may be the same or different. The target driving dataset can be a training set for training an autonomous driving model.

[0096] When the task configuration information includes multiple task names, a target task code set is screened out from the task code library and a corresponding basic code is screened out from the basic code library according to the multiple task names and the task execution order in the task configuration information; the original driving data set is processed by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library to obtain the target driving data set. The task corresponding to each task name in the task configuration information can be called a target task.

[0097] The task code library includes task codes of at least one of the following tasks: a first task, a second task, and a third task;

[0098] The first task is to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain a valid original driving data set;

[0099] The second task is to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain a high-precision original driving data set;

[0100] The third task is to convert the high-precision original driving dataset into a target driving dataset that meets the training requirements of the autonomous driving model.

[0101] Among them, according to the acquisition device to which each frame of raw driving data in the raw driving data set belongs, the method for judging whether the raw driving data is valid data includes: judging whether the raw driving data collected by the acquisition device within a continuous period of time has data missing according to the data format, data timestamp and other information of the data collected by the acquisition device, if there is data missing, the raw driving data collected by the acquisition device during this period is invalid data, if there is no data missing, the raw driving data collected by the acquisition device during this period is valid data. After obtaining the valid raw driving data set, if it is determined that the high-precision positioning algorithm is not used during the acquisition process of the valid raw driving data set, the low-precision positioning result in the valid raw driving data set can be modified to a high-precision positioning result. Since the positioning position in the high-precision positioning result is different from the positioning position in the low-precision positioning result, it is also necessary to modify the corresponding high-precision map based on the obtained high-precision positioning result to obtain a high-precision raw driving data set.

[0102] In one embodiment, when the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code call library, the online task code of the target task in the online code call library replaces the corresponding task code in the task code library and is added to the target task code set.

[0103] Detailed information not recorded in the embodiments of the present application can be found in the above-mentioned system embodiments and will not be repeated here.

[0104] The vehicle driving data processing method provided in the embodiment of the present application can provide a system for freely selecting tasks and task execution order. After the user sets the task configuration information on the client side of the system according to his own needs, the task scheduling module in the server side of the system can schedule the target task according to the task configuration information. The original driving data set is processed by executing the basic code of the target task and the task code of the target task to obtain the target driving data set. The entire process does not require manual code writing, which not only saves labor costs but also improves the efficiency of users in obtaining the target driving data set.

[0105] Based on the above method embodiment, another embodiment of the present application provides a vehicle driving data processing device, which can be applied to a server, such as Figure 4 As shown, the device comprises:

[0106] A receiving unit 310 is used to receive task configuration information and an original driving data set to be processed sent by a client, wherein the task configuration information includes at least one task name selected based on a task configuration interface output by the client and a task execution order set when multiple task names are selected, and each task is an algorithm for processing driving data;

[0107] The task execution unit 320 is used to schedule the target task according to the task configuration information, and process the original driving data set by executing the basic code of the target task and the task code of the target task to obtain the target driving data set, wherein the task code depends on the basic code when executing.

[0108] In a possible implementation, the task execution unit 320 includes:

[0109] A screening module, for screening out a target task code set from a task code library and a corresponding basic code from a basic code library according to the multiple task names and the task execution order in the task configuration information when the task configuration information includes multiple task names;

[0110] An execution module is used to process the original driving data set by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library to obtain the target driving data set.

[0111] In a possible implementation, the task code library includes task codes of at least one of the following tasks: a first task, a second task, and a third task;

[0112] The first task is used to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain the valid original driving data set;

[0113] The second task is used to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain the high-precision original driving data set;

[0114] The third task is used to convert the high-precision original driving data set into the target driving data set that meets the training requirements of the autonomous driving model.

[0115] In one possible implementation, the task execution unit 320 is also used to replace the corresponding task code in the task code library with the online task code of the target task in the online code calling library, and add it to the target task code set when the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code calling library.

[0116] In a possible implementation manner, the task configuration information also includes a user-defined task script.

[0117] The vehicle driving data processing device provided in the embodiment of the present application can provide a system for freely selecting tasks and task execution order. After the user sets the task configuration information on the client side of the system according to his own needs, the task scheduling module in the server side of the system can schedule the target task according to the task configuration information, and the original driving data set is processed by executing the basic code of the target task and the task code of the target task to obtain the target driving data set. The entire process does not require manual code writing, which not only saves labor costs but also improves the efficiency of users in obtaining the target driving data set.

[0118] Based on the above method embodiments, another embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any of the above embodiments is implemented.

[0119] Based on the above method embodiment, another embodiment of the present application provides an electronic device or a computer device, such as Figure 5 As shown, including:

[0120] one or more processors 410;

[0121] The processor 410 is coupled to a storage device 420, and the storage device 420 is used to store one or more programs;

[0122] When the one or more programs are executed by the one or more processors 410, the electronic device or computer device implements the method described in any of the above embodiments.

[0123] Based on the above embodiments, another embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed on a computer or a processor, the computer or the processor executes the method described in any of the above embodiments.

[0124] The above device embodiment corresponds to the method embodiment and has the same technical effect as the method embodiment. For specific description, please refer to the method embodiment. The device embodiment is obtained based on the method embodiment. For specific description, please refer to the method embodiment part, which will not be repeated here. It can be understood by those skilled in the art that the accompanying drawings are only schematic diagrams of one embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present application.

[0125] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application 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 embodiments of the present application.

Claims

1. A vehicle driving data processing system, characterized in that: The system includes a client and a server, wherein the server includes a container cluster management module, a task scheduling module and a basic code library; The client is used to output a task configuration interface for selecting tasks, receive task configuration information set based on the task configuration interface and receive an original driving data set to be processed, and send the task configuration information and the original driving data set to the server, wherein the task configuration information includes at least one task name selected based on the task configuration interface and a task execution order set when multiple task names are selected; The container cluster management module is used to deploy and manage the container cluster of the system, wherein each of the tasks is executed on a container, and each of the tasks is an algorithm for processing driving data; The basic code library is used to provide the basic code required to perform each of the tasks; The task scheduling module is used to schedule the target task according to the task configuration information, and process the original driving data set by executing the basic code of the target task and the task code of the target task to obtain the target driving data set, wherein the task code depends on the basic code when executing.

2. The system according to claim 1, characterized in that The task scheduling module is used to, when the task configuration information includes multiple task names, filter out a target task code set from a task code library according to the multiple task names and the task execution order in the task configuration information, and process the original driving data set by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library to obtain the target driving data set.

3. The system according to claim 2, characterized in that The task code library includes a task code of at least one of the following tasks: a first task, a second task, and a third task; The first task is used to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain a valid original driving data set; The second task is used to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain the high-precision original driving data set; The third task is used to convert the high-precision original driving data set into the target driving data set that meets the training requirements of the autonomous driving model.

4. The system according to claim 2, characterized in that The system further comprises: Online code calling library, including callable online task codes; The task scheduling module is used to replace the corresponding task code in the task code library with the online task code of the target task in the online code calling library and add it to the target task code set when the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code calling library.

5. The system according to any one of claims 1 to 4, characterized in that: The task configuration information also includes a user-defined task script.

6. A method for processing vehicle driving data, characterized in that: The method comprises: Receiving task configuration information and an original driving data set to be processed sent by a client, wherein the task configuration information includes at least one task name selected based on a task configuration interface output by the client and a task execution order set when multiple task names are selected, and each task is an algorithm for processing driving data; The target task is scheduled according to the task configuration information, and the original driving data set is processed by executing a basic code of the target task and a task code of the target task to obtain a target driving data set, wherein the task code depends on the basic code when executing.

7. The method according to claim 6, characterized in that Scheduling a target task according to the task configuration information, and processing the original driving data set by executing a basic code of the target task and a task code of the target task to obtain a target driving data set, including: When the task configuration information includes a plurality of task names, according to the plurality of task names and the task execution order in the task configuration information, a target task code set is screened out from a task code library, and a corresponding basic code is screened out from a basic code library; The target driving data set is obtained by processing the original driving data set by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library.

8. The method according to claim 7, characterized in that The task code library includes a task code of at least one of the following tasks: a first task, a second task, and a third task; The first task is used to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain the valid original driving data set; The second task is used to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain the high-precision original driving data set; The third task is used to convert the high-precision original driving data set into the target driving data set that meets the training requirements of the autonomous driving model.

9. The method according to claim 7, characterized in that: The method further comprises: When the task code of the target task obtained from the task code library is different from the online task code of the target task in the online code calling library, the online task code of the target task in the online code calling library replaces the corresponding task code in the task code library and is added to the target task code set.

10. The method according to any one of claims 6 to 9, characterized in that: The task configuration information also includes a user-defined task script.

11. A vehicle driving data processing device, characterized in that: The device comprises: A receiving unit, configured to receive task configuration information and an original driving data set to be processed sent by a client, wherein the task configuration information includes at least one task name selected based on a task configuration interface output by the client and a task execution order set when multiple task names are selected, and each task is an algorithm for processing driving data; A task execution unit is used to schedule a target task according to the task configuration information, and process the original driving data set by executing a basic code of the target task and a task code of the target task to obtain a target driving data set, wherein the task code depends on the basic code when executing.

12. The device according to claim 11, characterized in that The task execution unit comprises: A screening module, for screening out a target task code set from a task code library and a corresponding basic code from a basic code library according to the multiple task names and the task execution order in the task configuration information when the task configuration information includes multiple task names; An execution module is used to process the original driving data set by executing the task code of the target task in the target task code set and the corresponding basic code in the basic code library to obtain the target driving data set.

13. The device according to claim 12, characterized in that The task code library includes a task code of at least one of the following tasks: a first task, a second task, and a third task; The first task is used to determine whether the original driving data is valid data according to the acquisition device to which each frame of original driving data in the original driving data set belongs, so as to obtain the valid original driving data set; The second task is used to modify the low-precision positioning results in the valid original driving data set into high-precision positioning results, and modify the corresponding high-precision map based on the obtained high-precision positioning results to obtain the high-precision original driving data set; The third task is used to convert the high-precision original driving data set into the target driving data set that meets the training requirements of the autonomous driving model.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 6 to 10 is implemented.

15. An electronic device, characterized in that: The electronic device comprises: one or more processors; The processor is coupled to a storage device, and the storage device is used to store one or more programs; When the one or more programs are executed by the one or more processors, the electronic device implements the method according to any one of claims 6 to 10.

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