CN115423117A - Data processing method and device, vehicle, storage medium and equipment - Google Patents
Data processing method and device, vehicle, storage medium and equipment Download PDFInfo
- Publication number
- CN115423117A CN115423117A CN202211052545.8A CN202211052545A CN115423117A CN 115423117 A CN115423117 A CN 115423117A CN 202211052545 A CN202211052545 A CN 202211052545A CN 115423117 A CN115423117 A CN 115423117A
- Authority
- CN
- China
- Prior art keywords
- data set
- vehicle
- perception
- transition
- transformation processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a data processing method, a device, a vehicle, a storage medium and equipment, which are applied to automatic driving, wherein the data processing method comprises the following steps: performing primary transformation processing on a current data set to obtain a transition data set, wherein the primary transformation can enable data distribution of source data sets prestored in the transition data set to be consistent; inputting the transitional data set into a preset perception model, and outputting a transitional perception result, wherein the perception model is obtained by training based on the source data set; and performing secondary transformation processing on the transitional sensing result to obtain a target sensing result, wherein the primary transformation processing is inverse transformation processing of the secondary transformation processing. The data processing method, the data processing device, the storage medium and the equipment can greatly shorten the research and development period and reduce the manpower consumption.
Description
Technical Field
The present application relates to the field of automatic driving of automobiles, and in particular, to a data processing method and apparatus, a vehicle, a storage medium, and a device.
Background
In the field of intelligent driving perception, the laser perception problem is solved by using a deep learning algorithm, but the deep learning method usually needs to build a data acquisition vehicle firstly, then acquire a large amount of data, label the large amount of data and finally use a model trained by the data. In the process, a data acquisition vehicle is simply set up, data is acquired and data is marked, and huge time and labor cost are spent. However, once the vehicle model changes or the installation position and the number of sensing elements such as a laser radar change, the distribution of laser point cloud data generated by subsequent vehicle models and the originally trained laser point cloud data may be inconsistent. If the originally trained model is directly applied to the vehicle type, the perception effect is greatly reduced; if new laser point cloud data is collected again and the model is retrained, a large amount of time and manpower are consumed, and the research and development period is prolonged.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention provides a data processing method, apparatus, vehicle, storage medium, and device to reduce time consumption and labor costs.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a data processing method is applied to automatic driving, and comprises the following steps:
performing primary transformation processing on a current data set to obtain a transition data set, wherein the primary transformation enables data distribution of source data sets prestored in the transition data set to be consistent;
inputting the transition data set into a preset perception model, and outputting a transition perception result, wherein the perception model is obtained based on the source data set training;
and performing secondary transformation processing on the transitional sensing result to obtain a target sensing result, wherein the primary transformation processing is inverse transformation processing of the secondary transformation processing.
Optionally, the source data set is acquired by combined collection of sensing elements arranged on a vehicle of a first vehicle type, and the current data set is acquired by combined collection of sensing elements arranged on a vehicle of a second vehicle type, where the first vehicle type and the second vehicle type are different vehicle types.
Optionally, the source data set is acquired by collecting a first sensing element combination arranged on a vehicle of a first vehicle type, and the current data set is acquired by collecting a second sensing element combination arranged on the vehicle of the first vehicle type, where the first sensing element combination and the second sensing element combination have different mounting positions and/or sensing element numbers on the first vehicle type.
Optionally, the method for establishing the perceptual model includes:
establishing an initial deep learning model;
labeling a source data set to obtain a first data set;
randomly dividing the first data set into a training sample and a testing sample according to a preset proportion;
training the initial deep learning model through the training sample to obtain a trained deep learning model;
and testing the trained deep learning models through the test sample, wherein the tested deep learning models are the perception models.
Optionally, in the process of training the initial deep learning model through the training sample, if the convergence value of the loss function reaches the preset convergence value, the training is stopped, and the trained deep learning model is obtained.
Correspondingly, the invention also provides a data processing device, which is applied to automatic driving and comprises:
the primary transformation module is used for carrying out primary transformation processing on the current data set to obtain a transition data set, and the primary transformation can enable the data distribution of source data sets prestored in the transition data set to be consistent;
the reasoning module is used for inputting the transition data set into a preset perception model and outputting a transition perception result, wherein the perception model is obtained by training based on the source data set; and
and the secondary transformation module is used for carrying out secondary transformation processing on the transition perception result to obtain a target perception result, wherein the primary transformation processing is inverse transformation processing of the secondary transformation processing.
Correspondingly, the invention also provides a vehicle, which is an automatic driving vehicle, and the vehicle comprises:
a vehicle body;
a sensing element assembly, wherein the sensing element assembly comprises a plurality of sensing elements arranged on the vehicle body, and is used for acquiring the current data set; and
a data processing apparatus, said data processing apparatus being any of the data processing apparatuses described above.
To achieve the above object, the present invention also provides an electronic device, comprising:
one or more processors;
memory for storing one or more programs that, when executed by the one or more processors, cause the electronic device to perform the data processing method as any one of the above.
To achieve the above object, the present invention further provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are executed by a processor to implement any one of the data processing methods described above.
The invention has the beneficial effects that:
according to the data processing method, the data processing device, the vehicle, the storage medium and the equipment, when the vehicle type changes or the installation position, the number and the like of the sensing elements change, a new sensing model does not need to be established again, and the marking and the training are not needed again, so that the research and development period is greatly shortened, and the labor consumption is reduced; the invention can directly apply the originally trained perception model to a new vehicle type, and can also be applied to the vehicle type with the changed installation position and quantity of perception elements, and the perception performance can still be ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a block flow diagram of a data processing method shown in an exemplary embodiment of the present application;
FIG. 2 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure of the present specification, wherein the following description is made for the embodiments of the present invention with reference to the accompanying drawings and the preferred embodiments. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
As shown in fig. 1, in an exemplary embodiment, a data processing method for automatic driving includes:
s300, carrying out primary transformation processing on the current data set T1 to obtain a transition data set T2, wherein the primary transformation enables data distribution of source data N sets prestored in the transition data set to be consistent;
s500, inputting the transition data set T2 into a preset perception model M, and outputting a transition perception result J1, wherein the perception model M is obtained by training based on the source data set N;
s700, carrying out secondary transformation processing on the transitional sensing result J1 to obtain a target sensing result J2, wherein the primary transformation processing is inverse transformation processing of the secondary transformation processing.
Here, "the primary transform processing is the inverse transform processing of the secondary transform processing" means: and after the transition data set T2 is subjected to secondary transformation processing, the obtained data set is the current data set T1.
By the data processing method, when the vehicle type is changed or the installation position, the number and the like of the sensing elements are changed, a new sensing model does not need to be established again, the marking and the training are not needed again, and the research and development period is greatly shortened; by the data processing method, the originally trained perception model M can be directly applied to new vehicle types, and can also be applied to vehicle types with the changed installation positions and quantity of perception elements, and the perception performance can still be ensured.
It should be noted that, if the current data set T1 is directly input into the preset sensing model M, the output sensing result is greatly discounted, but in the present invention, the current data set T1 is changed into the transition data set T2 with the data distribution consistent with the data distribution of the source data set N through one change processing, and then the T2 is input into the preset sensing model M, because the data distribution of the input transition data set T2 is consistent with the source data set, the sensing performance is ensured; when the vehicle type is changed or the vehicle type is not changed, but the installation positions and the number of the sensing elements are changed, the transition sensing result J1 needs to be subjected to secondary transformation processing to obtain a target sensing result J2, so that the data type of the target sensing result J2 can be adapted to the current vehicle type or the state of the current sensing element, and the sensing result adapted to the data type is output.
In some embodiments, the source data set is acquired by a combination of sensing elements disposed on a vehicle of a first vehicle type, and the current data set is acquired by a combination of sensing elements disposed on a vehicle of a second vehicle type, where the first vehicle type and the second vehicle type are different vehicle types.
In other embodiments, the source data set is acquired by a first sensing element combination set on a vehicle of a first vehicle type, and the current data set is acquired by a second sensing element combination set on the vehicle of the first vehicle type, where the first sensing element combination and the second sensing element combination have different mounting positions and/or sensing element numbers on the first vehicle type.
In the above and following embodiments, the sensing element combination may include one or more sensing elements, and the sensing element may be a laser radar, a camera, or the like.
In some embodiments, the method for establishing the perception model comprises:
s110, establishing an initial deep learning model M 0 ；
S120, labeling the source data set N to obtain a first data set N 1 ；
S130, setting the first data set N 1 Randomly dividing the training samples into training samples X according to a preset proportion 1 And test specimen C 1 ；
S140, passing the training sample X 1 Training the initial deep learning model M 0 Obtaining a trained deep learning model M 1 ；
S150, passing the test sample C 1 Testing the trained deep learning models M 1 After the testThe deep learning model of (2) is the perception model M.
It should be further noted that the source data set in the above description may be obtained by building a data collection vehicle, and if the source data set and the current data set correspond to different vehicle types, the first vehicle type is the data collection vehicle.
In some embodiments, X is trained by the training sample 1 The initial deep learning model M 0 In the process, if the convergence value of the LOSS function (LOSS) reaches a preset convergence value, stopping training to obtain a trained deep learning model M 1 。
Correspondingly, the invention also provides a data processing device, which is applied to automatic driving and comprises:
the primary transformation module is used for carrying out primary transformation processing on the current data set to obtain a transition data set, and the primary transformation can enable the data distribution of source data sets prestored in the transition data set to be consistent;
the reasoning module is used for inputting the transition data set into a preset perception model and outputting a transition perception result, wherein the perception model is obtained by training based on the source data set; and
and the secondary transformation module is used for carrying out secondary transformation processing on the transition perception result to obtain a target perception result, wherein the primary transformation processing is inverse transformation processing of the secondary transformation processing.
It should be noted that the data processing apparatus provided in the foregoing embodiment and the data processing method provided in the foregoing embodiment belong to the same concept, and specific ways for the modules and units to perform operations have been described in detail in the method embodiment, and are not described herein again. In practical applications, the road condition refreshing apparatus provided in the above embodiment may distribute the above functions through different functional modules according to needs, that is, divide the internal structure of the apparatus into different functional modules to complete all or part of the above described functions, which is not limited herein.
Correspondingly, the invention also discloses a vehicle which is an automatic driving vehicle and comprises a vehicle body, a sensing element assembly and a data processing device, wherein the sensing element assembly comprises a plurality of sensing elements arranged on the vehicle body, and is used for acquiring the current data set; the data processing device may be the data processing device provided in any of the embodiments described above.
An embodiment of the present application further provides an electronic device, including: one or more processors; a memory for storing one or more programs, which when executed by the one or more processors, cause the electronic device to implement the data processing methods provided in the above-described embodiments.
FIG. 2 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application. It should be noted that the computer system 1200 of the electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 2, the computer system 1200 includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM1203, various programs and data necessary for system operation are also stored. The CPU 1201, ROM 1202, and RAM1203 are connected to each other by a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.
The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output section 1207 including a Display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a Network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1209, and/or installed from the removable medium 1211. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1201.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may comprise a propagated data signal with a computer-readable computer program embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
Another aspect of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute the data processing method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist separately without being incorporated in the electronic device.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the data processing method provided in the above embodiments.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (9)
1. A data processing method, applied to automatic driving, the data processing method comprising:
performing primary transformation processing on a current data set to obtain a transition data set, wherein the primary transformation enables data distribution of source data sets prestored in the transition data set to be consistent;
inputting the transition data set into a preset perception model, and outputting a transition perception result, wherein the perception model is obtained based on the source data set training;
and performing secondary transformation processing on the transitional sensing result to obtain a target sensing result, wherein the primary transformation processing is inverse transformation processing of the secondary transformation processing.
2. The automatic driving perception method according to claim 1, characterized in that: the source data set is acquired through combination acquisition of sensing elements arranged on a vehicle of a first vehicle type, and the current data set is acquired through combination acquisition of sensing elements arranged on a vehicle of a second vehicle type, wherein the first vehicle type and the second vehicle type are different vehicle types.
3. The automatic driving perception method according to claim 1, characterized in that: the source data set is acquired through a first sensing element combination arranged on a vehicle of a first vehicle type, the current data set is acquired through a second sensing element combination arranged on the vehicle of the first vehicle type, and the mounting positions and/or the number of sensing elements of the first sensing element combination and the second sensing element combination on the first vehicle type are different.
4. The automatic driving perception method according to claim 1, characterized in that: the method for establishing the perception model comprises the following steps:
establishing an initial deep learning model;
labeling a source data set to obtain a first data set;
randomly dividing the first data set into a training sample and a testing sample according to a preset proportion;
training the initial deep learning model through the training sample to obtain a trained deep learning model;
and testing the trained deep learning models through the test sample, wherein the tested deep learning models are the perception models.
5. The automatic driving perception method according to claim 4, characterized in that: and in the process of training the initial deep learning model through the training sample, if the convergence value of the loss function reaches a preset convergence value, stopping training to obtain the trained deep learning model.
6. A data processing apparatus, for use in autonomous driving, the data processing apparatus comprising:
the primary transformation module is used for performing primary transformation processing on a current data set to obtain a transition data set, and the primary transformation can enable data distribution of source data sets prestored in the transition data set to be consistent;
the reasoning module is used for inputting the transition data set into a preset perception model and outputting a transition perception result, wherein the perception model is obtained by training based on the source data set; and
and the secondary transformation module is used for carrying out secondary transformation processing on the transition perception result to obtain a target perception result, wherein the primary transformation processing is inverse transformation processing of the secondary transformation processing.
7. A vehicle, characterized in that the vehicle is an autonomous vehicle, the vehicle comprising:
a vehicle body;
a sensing element assembly, wherein the sensing element assembly comprises a plurality of sensing elements arranged on the vehicle body, and is used for acquiring the current data set; and
a data processing apparatus, said data processing apparatus being the data processing apparatus of claim 6.
8. An electronic device, characterized in that the electronic device comprises:
one or more processors;
memory storing one or more programs that, when executed by the one or more processors, cause the electronic device to perform the automated driving perception method of any of claims 1-5.
9. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the method of automated driving perception according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211052545.8A CN115423117A (en) | 2022-08-31 | 2022-08-31 | Data processing method and device, vehicle, storage medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211052545.8A CN115423117A (en) | 2022-08-31 | 2022-08-31 | Data processing method and device, vehicle, storage medium and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115423117A true CN115423117A (en) | 2022-12-02 |
Family
ID=84199678
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211052545.8A Pending CN115423117A (en) | 2022-08-31 | 2022-08-31 | Data processing method and device, vehicle, storage medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115423117A (en) |
-
2022
- 2022-08-31 CN CN202211052545.8A patent/CN115423117A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110780608B (en) | Simulation test method and device | |
| CN112348029A (en) | Local map adjusting method, device, equipment and computer readable medium | |
| CN116934769A (en) | Interactive segmentation model training method, annotation data generation method and equipment | |
| CN113592033B (en) | Oil tank image recognition model training method, oil tank image recognition method and device | |
| CN114010180B (en) | Magnetic resonance rapid imaging method and device based on convolutional neural network | |
| CN115423117A (en) | Data processing method and device, vehicle, storage medium and equipment | |
| CN113409393B (en) | Method and device for identifying traffic sign | |
| CN115934572A (en) | Vehicle control module testing method and system, computer equipment and storage medium | |
| CN116148951B (en) | Training method, forecasting method and device for air target pollutant component forecasting model | |
| CN114926745B (en) | Domain feature mapping small sample SAR target recognition method | |
| CN109271954B (en) | Method and device for detecting reliability of model | |
| CN116090252A (en) | Matlab-based method, device, equipment and medium for automatically generating input interface model | |
| CN115563734A (en) | Fault-tolerant time interval calculation method and device, electronic equipment and storage medium | |
| CN114416441A (en) | Real-time database automatic testing method and system, electronic equipment and storage medium | |
| CN115221069A (en) | Electronic control unit upgrade test method, system, electronic device and storage medium | |
| CN115469899A (en) | Firmware upgrading method, device, equipment, medium and vehicle-mounted system | |
| CN114343673B (en) | Method, medium and equipment for processing brain wave signals crossing tested motor imagery | |
| CN115237096A (en) | Calibration method and device for automatic driving parameters, electronic equipment and medium | |
| CN116089310A (en) | Automatic driving data abstraction software testing method, system, equipment and storage medium | |
| CN113806236B (en) | Method and system for one-machine multi-control of cloud real machine in mobile test field | |
| CN115202916A (en) | Test data acquisition method and system, electronic device and readable storage medium | |
| CN115407679A (en) | Test method, device, equipment and storage medium of automatic driving algorithm | |
| CN115630323A (en) | Method, device, equipment and storage medium for closed loop of pedestrian intention prediction data | |
| CN115421993A (en) | Auxiliary driving algorithm testing method and system, electronic equipment and readable storage medium | |
| CN118114893A (en) | Distribution method, device, medium and equipment of automatic driving road test resources |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |