CN109032103B - Method, device and equipment for testing unmanned vehicle and storage medium - Google Patents
Method, device and equipment for testing unmanned vehicle and storage medium Download PDFInfo
- Publication number
- CN109032103B CN109032103B CN201710431914.7A CN201710431914A CN109032103B CN 109032103 B CN109032103 B CN 109032103B CN 201710431914 A CN201710431914 A CN 201710431914A CN 109032103 B CN109032103 B CN 109032103B
- Authority
- CN
- China
- Prior art keywords
- test
- result
- algorithm module
- data
- vehicle
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
- G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses a method, a device, equipment and a storage medium for testing an unmanned vehicle, wherein the method comprises the following steps: acquiring test scene data acquired when a manually driven vehicle runs; dividing test scene data into input data and an expected result; when the algorithm module in the unmanned vehicle is updated, the input data is sent to the algorithm module to obtain a test result, the test result is compared with an expected result, and whether the test is passed or not is determined according to the comparison result. By adopting the scheme of the invention, the test can be completed quickly and effectively.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technology of unmanned vehicles, in particular to a method, a device, equipment and a storage medium for testing unmanned vehicles.
[ background of the invention ]
An unmanned vehicle, also referred to as an autonomous vehicle or the like, senses the environment around the vehicle by sensors, and controls the steering and speed of the vehicle based on the sensed road, vehicle position, obstacle information, and the like, so that the vehicle can safely and reliably travel on the road.
At the heart of the unmanned vehicle is an algorithm module called 'driving brain', which can comprise sub-modules of positioning, high-precision mapping, perception, decision and control, and the like.
The sub-modules are generally developed and maintained by different teams and are integrated together to be used as an algorithm module of the whole unmanned vehicle for publishing and deployment.
Because a plurality of complex sub-modules are involved and the existing software engineering adopts an agile iterative development mode, the iterative cycle of the algorithm module is short and the updating (changing) is frequent.
Any modification of the algorithm module may introduce a new problem, and therefore, after each update, a test is required to determine whether the updated algorithm module can work normally, and accordingly, how to complete the test quickly and effectively is an urgent problem to be solved.
[ summary of the invention ]
In view of the above, the present invention provides a method, an apparatus, a device and a storage medium for testing an unmanned vehicle, which can complete a test quickly and efficiently.
The specific technical scheme is as follows:
a method of testing an unmanned vehicle, comprising:
acquiring test scene data acquired when a manually driven vehicle runs;
dividing the test scene data into input data and an expected result;
and when the algorithm module in the unmanned vehicle is updated, sending the input data to the algorithm module to obtain a test result, comparing the test result with the expected result, and determining whether the test is passed or not according to the comparison result.
According to a preferred embodiment of the present invention, the acquiring the test scenario data collected when the manually driven vehicle is running includes:
respectively acquiring test scene data acquired when the manually-driven vehicle runs according to different test scenes;
the dividing the test scenario data into input data and an expected result comprises:
for each test scene, dividing test scene data corresponding to the test scene into input data and an expected result;
when the algorithm module in the unmanned vehicle is updated, the input data is sent to the algorithm module to obtain a test result, the test result is compared with the expected result, and whether the test is passed or not is determined according to the comparison result, wherein the step of:
when the algorithm module of the unmanned vehicle is updated, the input data in the test scene data corresponding to each test scene is sent to the algorithm module respectively according to each test scene to obtain a test result, the test result is compared with an expected result in the test scene data corresponding to the test scene, and whether the test in the test scene passes or not is determined according to the comparison result.
According to a preferred embodiment of the invention, the method further comprises:
and acquiring an updated algorithm module deployed to the unmanned vehicle in a remote wireless transmission mode.
According to a preferred embodiment of the present invention, the test scenario data includes: sensor data, driver control information and vehicle running track information;
the dividing the test scenario data into input data and an expected result comprises:
the sensor data is used as the input data, and the driver operation information and the vehicle running track information are used as the expected result.
According to a preferred embodiment of the present invention, said sending said input data to said algorithm module comprises:
sending the sensor data to the algorithm module by simulating each sensor on the unmanned vehicle;
the comparing the test result to the expected result comprises:
comparing vehicle handling information in the test result with the driver handling information in the expected result;
and comparing the vehicle running track information in the test result with the vehicle running track information in the expected result.
A test apparatus for an unmanned vehicle, comprising: a preprocessing unit and a testing unit;
the preprocessing unit is used for acquiring test scene data acquired when the manually driven vehicle runs and dividing the test scene data into input data and expected results;
and the test unit is used for sending the input data to the algorithm module to obtain a test result when the algorithm module in the unmanned vehicle is updated, comparing the test result with the expected result, and determining whether the test is passed or not according to the comparison result.
In accordance with a preferred embodiment of the present invention,
the pre-processing unit is further configured to,
respectively acquiring test scene data acquired when the manually-driven vehicle runs according to different test scenes;
for each test scene, dividing test scene data corresponding to the test scene into input data and an expected result;
the test unit is further configured to,
when the algorithm module of the unmanned vehicle is updated, the input data in the test scene data corresponding to each test scene is sent to the algorithm module respectively according to each test scene to obtain a test result, the test result is compared with an expected result in the test scene data corresponding to the test scene, and whether the test in the test scene passes or not is determined according to the comparison result.
According to a preferred embodiment of the present invention, the apparatus further comprises: an update unit;
and the updating unit is used for acquiring the updated algorithm module sent by the remote wireless transmission mode and deploying the updated algorithm module to the unmanned vehicle.
According to a preferred embodiment of the present invention, the test scenario data includes: sensor data, driver control information and vehicle running track information;
the preprocessing unit takes the sensor data as the input data, and takes the driver manipulation information and the vehicle travel track information as the desired result.
According to a preferred embodiment of the present invention, the test unit comprises: a sensor simulation subunit and a result verification subunit;
the sensor simulation subunit is used for sending the sensor data to the algorithm module by simulating each sensor on the unmanned vehicle when the algorithm module in the unmanned vehicle is updated;
the result verification subunit is configured to obtain a test result, compare the vehicle operation information in the test result with the driver operation information in the expected result, compare the vehicle travel track information in the test result with the vehicle travel track information in the expected result, and determine whether the test is passed according to the comparison result.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method as set forth above.
Based on the introduction, the scheme of the invention can acquire the test scene data acquired when the manually-driven vehicle runs in advance, and divide the test scene data into the input data and the expected result, so that when the algorithm module in the unmanned vehicle is updated, the input data can be sent to the algorithm module to obtain the test result, the test result is compared with the expected result, whether the test is passed or not is determined according to the comparison result, and it can be seen that the test can be completed quickly and effectively by using the test scene data when the algorithm module is updated every time, and the acquired test scene data can be used repeatedly without being acquired again every time, thereby improving the test efficiency.
[ description of the drawings ]
Fig. 1 is a flowchart of a first embodiment of a method for testing an unmanned vehicle according to the present invention.
Fig. 2 is a flowchart of a second embodiment of a method for testing an unmanned vehicle according to the present invention.
Fig. 3 is a schematic structural diagram of a testing apparatus of an unmanned vehicle according to an embodiment of the present invention.
FIG. 4 illustrates a block diagram of an exemplary computer system/server 12 suitable for use in implementing embodiments of the present invention.
[ detailed description ] embodiments
Aiming at the problems in the prior art, the invention provides a test mode of an unmanned vehicle, test scene data is constructed in advance, when an algorithm module in the unmanned vehicle is updated, the updated algorithm module is automatically deployed on the unmanned vehicle in a remote wireless transmission mode, and the test is completed by using the test scene data, so that various control indexes and the like of the unmanned vehicle on a test road are verified, and the aim of quickly verifying the driving brain of the unmanned vehicle is fulfilled.
In order to make the technical solution of the present invention clearer and more obvious, the solution of the present invention is further described below by referring to the drawings and examples.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a first embodiment of a testing method for an unmanned vehicle according to the present invention, as shown in fig. 1, including the following detailed implementation.
In 101, test scene data collected while a human-driven vehicle is traveling is acquired.
An experienced excellent driver can be selected to manually drive the data acquisition vehicle to run on an actual road, so that the required test scene data can be acquired.
The data acquisition vehicle needs to be equipped with various sensors with the same configuration as the unmanned vehicle, such as: laser radar, image sensor, millimeter wave radar, ultrasonic radar, infrared sensor, Global Positioning System (GPS), Inertial Measurement Unit (IMU), and the like.
The collected test scenario data may include: sensor data, driver handling information, vehicle travel track information, and the like.
The sensor data may include output data of each sensor, internal and external parameters, and the like, and the internal and external parameters of the sensor may include relative position information of the sensor on the vehicle, a model of the sensor itself, functional performance parameters, and the like.
The driver control information refers to information such as control action and time of a driver in the driving process, such as steering angle, braking time and intensity, refueling time and intensity and the like.
In 102, test scenario data is divided into input data and expected results.
The collected test scenario data may be further divided into input data and expected results.
Specifically, sensor data may be used as input data, and driver manipulation information and vehicle travel track information may be used as desired results.
In 103, when the algorithm module in the unmanned vehicle is updated, the input data is sent to the algorithm module to obtain the test result.
When any one or more of the positioning sub-modules, the high-precision map sub-modules, the perception sub-modules, the decision sub-modules and the control sub-modules in the algorithm module are changed, the algorithm module can be considered to be updated, and accordingly, the updated algorithm module can be deployed on the unmanned vehicle through the cloud server in a remote wireless transmission mode.
And then, the sensor data serving as input data can be sent to the updated algorithm module by simulating each sensor on the unmanned vehicle, so that the algorithm module can perform decision control and other operations according to the acquired sensor data and the like, and a test result is obtained.
The working modes of all sensors on the unmanned vehicle in actual work can be simulated, and the sensor data is sent to the algorithm module.
At 104, the test result is compared to an expected result, and a determination is made as to whether the test passed based on the comparison.
The obtained test result may include vehicle operation information, vehicle running track information, and the like, and the vehicle operation information in the test result may be compared with the driver operation information in the expected result, and the vehicle running track information in the test result may be compared with the vehicle running track information in the expected result.
It may be determined whether the test passed based on the comparison, for example, if the test result matches the expected result, the test is deemed to pass, or if the test result differs from the expected result within an acceptable range, the test is deemed to pass.
And the related data of the test, such as the middle process of the test, the test log, the test result and the like, can be sent to the cloud server for storage.
Based on the introduction, by adopting the scheme of the embodiment, the test scene data acquired when the manually-driven vehicle runs can be acquired in advance, and the test scene data is divided into the input data and the expected result, so that when the algorithm module in the unmanned vehicle is updated, the input data can be sent to the algorithm module, the test result is obtained, the test result is compared with the expected result, whether the test is passed or not is determined according to the comparison result, and it can be seen that when the algorithm module is updated each time, the test can be completed quickly and effectively by using the test scene data.
Moreover, by adopting the scheme of the embodiment, the collected test scene data can be repeatedly used for many times without being acquired again every time, so that the test efficiency is improved.
In addition, by adopting the scheme of the embodiment, the tested unmanned vehicle does not need to be provided with sensors, so that the implementation cost is reduced.
Furthermore, by adopting the scheme of the embodiment, the algorithm module can be updated in a remote deployment mode, so that the method is more convenient and faster, the algorithm development progress is greatly improved, and the product release period is further shortened.
On the basis, different test scenes can be divided, and the test is respectively carried out aiming at the different test scenes, so that the applicability of the updated algorithm module to the different test scenes and the like can be comprehensively tested.
Accordingly, fig. 2 is a flowchart of a second embodiment of the testing method for the unmanned vehicle according to the present invention, and as shown in fig. 2, the following detailed implementation is included.
In 201, test scene data collected when the manually driven vehicle runs are acquired respectively for different test scenes.
Different test scenarios may include: the vehicle can pass through a crossroad without vehicles and pedestrians in a straight-going way, overtake when an obstacle exists in the front of the crossroad, stop for waiting when passing through the crossroad with a bright red light, brake when a vehicle stops in the front of a one-way road and the like.
And respectively acquiring test scene data aiming at different test scenes.
The test scenario data may include: sensor data, driver handling information, vehicle travel track information, and the like.
At 202, test scenario data corresponding to each test scenario is divided into input data and an expected result for each test scenario.
For example, for a test scenario of "straight-through passing through an intersection without vehicles and pedestrians", test scenario data corresponding to the test scenario is divided into input data and an expected result.
Similarly, for a test scenario of "overtaking in the case of an obstacle ahead", test scenario data corresponding to the test scenario is divided into input data and an expected result.
Specifically, the sensor data in the test scenario data may be used as input data, and the driver manipulation information and the vehicle travel track information in the test scenario data may be used as expected results.
In 203, when the algorithm module of the unmanned vehicle is updated, for each test scenario, the input data in the test scenario data corresponding to the test scenario is sent to the algorithm module, so as to obtain a test result, the test result is compared with an expected result in the test scenario data corresponding to the test scenario, and whether the test in the test scenario passes or not is determined according to the comparison result.
For example, for a test scene of 'straight-through passing through an intersection without vehicles and pedestrians', the sensor data in the test scene data corresponding to the test scene can be sent to the updated algorithm module by simulating each sensor on the unmanned vehicle, the algorithm module can perform decision control and other operations according to the acquired sensor data and the like, so as to obtain a test result, then, vehicle operation and control information in the test result can be compared with driver operation and control information in the test scene data corresponding to the test scene, vehicle running track information in the test result is compared with vehicle running track information in the test scene data corresponding to the test scene, and whether the test in the test scene passes or not is determined according to the comparison result.
For other test scenarios, the processing can be performed in the above manner.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above is a description of method embodiments, and the embodiments of the present invention are further described below by way of apparatus embodiments.
Fig. 3 is a schematic structural diagram of a testing apparatus of an unmanned vehicle according to an embodiment of the present invention, as shown in fig. 3, including: a preprocessing unit 301 and a test unit 302.
The preprocessing unit 301 is configured to acquire test scenario data acquired when the manually driven vehicle runs, and divide the test scenario data into input data and an expected result.
The test unit 302 is configured to send input data to an algorithm module (driving brain) in the unmanned vehicle when the algorithm module is updated, obtain a test result, compare the test result with an expected result, and determine whether the test passes or not according to the comparison result.
An experienced excellent driver can be selected to manually drive the data acquisition vehicle to run on an actual road, so that the required test scene data can be acquired.
The data acquisition vehicle needs to be equipped with various sensors with the same configuration as the unmanned vehicle, such as: laser radar, image sensor, millimeter wave radar, ultrasonic radar, infrared sensor, GPS, IMU, and the like.
The collected test scenario data may include: sensor data, driver handling information, vehicle travel track information, and the like.
The sensor data may include output data of each sensor, internal and external parameters, and the like, and the internal and external parameters of the sensor may include relative position information of the sensor on the vehicle, a model of the sensor itself, functional performance parameters, and the like.
The driver control information refers to information such as control action and time of a driver in the driving process, such as steering angle, braking time and intensity, refueling time and intensity and the like.
After the preprocessing unit 301 obtains the test scene data collected when the manually driven vehicle is running, the test scene data can be further divided into input data and expected results.
Specifically, sensor data may be used as input data, and driver manipulation information and vehicle travel track information may be used as desired results.
As shown in fig. 3, the apparatus may further include: the updating unit 303, in addition, the testing unit 302 may specifically include: a sensor simulation subunit 3021 and a result verification subunit 3022.
The updating unit 303 may obtain the updated algorithm module transmitted by remote wireless transmission, and deploy the updated algorithm module to the unmanned vehicle.
When any one or more of the positioning sub-modules, the high-precision map sub-modules, the sensing sub-modules, the decision-making sub-modules and the control sub-modules in the algorithm module are changed, the algorithm module can be considered to be updated, and accordingly, the updated algorithm module can be sent to the updating unit 303 through the cloud server in a remote wireless transmission mode.
Then, the sensor simulation subunit 3021 may send the sensor data to the algorithm module by simulating each sensor on the unmanned vehicle, so that the algorithm module performs decision control and other operations according to the acquired sensor data and the like, thereby obtaining a test result.
The obtained test result may include vehicle operation information, vehicle travel track information, and the like, and the result verification subunit 3022 may compare the vehicle operation information in the test result with the driver operation information in the expected result, compare the vehicle travel track information in the test result with the vehicle travel track information in the expected result, and determine whether the test is passed or not according to the comparison result.
It can be seen that, by adopting the scheme of the embodiment, when the algorithm module is updated each time, the test can be completed quickly and effectively by using the test scene data.
Moreover, by adopting the scheme of the embodiment, the collected test scene data can be repeatedly used for many times without being acquired again every time, so that the test efficiency is improved.
In addition, by adopting the scheme of the embodiment, the tested unmanned vehicle does not need to be provided with sensors, so that the implementation cost is reduced.
Furthermore, by adopting the scheme of the embodiment, the algorithm module can be updated in a remote deployment mode, so that the method is more convenient and faster, the algorithm development progress is greatly improved, and the product release period is further shortened.
On the basis, different test scenes can be divided, and the test is respectively carried out aiming at the different test scenes, so that the applicability of the updated algorithm module to the different test scenes and the like can be comprehensively tested.
Accordingly, the preprocessing unit 301 may respectively obtain test scenario data acquired when the manually driven vehicle runs according to different test scenarios, and divide the test scenario data corresponding to each test scenario into input data and an expected result according to each test scenario.
Different test scenarios may include: the vehicle can pass through a crossroad without vehicles and pedestrians in a straight-going way, overtake when an obstacle exists in the front of the crossroad, stop for waiting when passing through the crossroad with a bright red light, brake when a vehicle stops in the front of a one-way road and the like.
When the algorithm module of the unmanned vehicle is updated, for each test scenario, the test unit 302 may respectively send the input data in the test scenario data corresponding to the test scenario to the algorithm module to obtain a test result, compare the test result with an expected result in the test scenario data corresponding to the test scenario, and determine whether the test in the test scenario passes or not according to the comparison result.
For a specific work flow of the apparatus embodiment shown in fig. 3, please refer to the corresponding description in the foregoing method embodiment, which is not repeated.
FIG. 4 illustrates a block diagram of an exemplary computer system/server 12 suitable for use in implementing embodiments of the present invention. The computer system/server 12 shown in FIG. 4 is only one example and should not be taken to limit the scope of use or functionality of embodiments of the present invention.
As shown in FIG. 4, computer system/server 12 is in the form of a general purpose computing device. The components of computer system/server 12 may include, but are not limited to: one or more processors (processing units) 16, a memory 28, and a bus 18 that connects the various system components, including the memory 28 and the processors 16.
Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The computer system/server 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The computer system/server 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the computer system/server 12, and/or with any devices (e.g., network card, modem, etc.) that enable the computer system/server 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the computer system/server 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 20. As shown in FIG. 4, network adapter 20 communicates with the other modules of computer system/server 12 via bus 18. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer system/server 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processor 16 executes various functional applications and data processing by executing programs stored in the memory 28, for example, implementing the method in the embodiment shown in fig. 1, that is, acquiring test scenario data collected when the unmanned vehicle is running, dividing the test scenario data into input data and expected results, sending the input data to the algorithm module when the algorithm module in the unmanned vehicle is updated, obtaining the test results, comparing the test results with the expected results, and determining whether the test passes or not according to the comparison results.
For specific implementation, please refer to the corresponding descriptions in the foregoing embodiments, which are not repeated.
The invention also discloses a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, will carry out the method as in the embodiment shown in fig. 1.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 or 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 context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of 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.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method, etc., can be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. A method of testing an unmanned vehicle, comprising:
acquiring test scene data acquired when a manually driven vehicle runs;
dividing the test scene data into input data and an expected result;
and when the algorithm module in the unmanned vehicle is updated, sending the input data to the algorithm module to obtain a test result, comparing the test result with the expected result, and determining whether the test is passed or not according to the comparison result.
2. The method of claim 1,
the acquiring of the test scene data collected when the manually-driven vehicle runs comprises:
respectively acquiring test scene data acquired when the manually-driven vehicle runs according to different test scenes;
the dividing the test scenario data into input data and an expected result comprises:
for each test scene, dividing test scene data corresponding to the test scene into input data and an expected result;
when the algorithm module in the unmanned vehicle is updated, the input data is sent to the algorithm module to obtain a test result, the test result is compared with the expected result, and whether the test is passed or not is determined according to the comparison result, wherein the step of:
when the algorithm module of the unmanned vehicle is updated, the input data in the test scene data corresponding to each test scene is sent to the algorithm module respectively according to each test scene to obtain a test result, the test result is compared with an expected result in the test scene data corresponding to the test scene, and whether the test in the test scene passes or not is determined according to the comparison result.
3. The method of claim 1,
the method further comprises the following steps:
and acquiring an updated algorithm module deployed to the unmanned vehicle in a remote wireless transmission mode.
4. The method of claim 1,
the test scenario data includes: sensor data, driver control information and vehicle running track information;
the dividing the test scenario data into input data and an expected result comprises:
the sensor data is used as the input data, and the driver operation information and the vehicle running track information are used as the expected result.
5. The method of claim 4,
the sending the input data to the algorithm module comprises:
sending the sensor data to the algorithm module by simulating each sensor on the unmanned vehicle;
the comparing the test result to the expected result comprises:
comparing vehicle handling information in the test result with the driver handling information in the expected result;
and comparing the vehicle running track information in the test result with the vehicle running track information in the expected result.
6. A test apparatus for an unmanned vehicle, comprising: a preprocessing unit and a testing unit;
the preprocessing unit is used for acquiring test scene data acquired when the manually driven vehicle runs and dividing the test scene data into input data and expected results;
and the test unit is used for sending the input data to the algorithm module to obtain a test result when the algorithm module in the unmanned vehicle is updated, comparing the test result with the expected result, and determining whether the test is passed or not according to the comparison result.
7. The apparatus of claim 6,
the pre-processing unit is further configured to,
respectively acquiring test scene data acquired when the manually-driven vehicle runs according to different test scenes;
for each test scene, dividing test scene data corresponding to the test scene into input data and an expected result;
the test unit is further configured to,
when the algorithm module of the unmanned vehicle is updated, the input data in the test scene data corresponding to each test scene is sent to the algorithm module respectively according to each test scene to obtain a test result, the test result is compared with an expected result in the test scene data corresponding to the test scene, and whether the test in the test scene passes or not is determined according to the comparison result.
8. The apparatus of claim 6,
the device further comprises: an update unit;
and the updating unit is used for acquiring the updated algorithm module sent by the remote wireless transmission mode and deploying the updated algorithm module to the unmanned vehicle.
9. The apparatus of claim 6,
the test scenario data includes: sensor data, driver control information and vehicle running track information;
the preprocessing unit takes the sensor data as the input data, and takes the driver manipulation information and the vehicle travel track information as the desired result.
10. The apparatus of claim 9,
the test unit comprises: a sensor simulation subunit and a result verification subunit;
the sensor simulation subunit is used for sending the sensor data to the algorithm module by simulating each sensor on the unmanned vehicle when the algorithm module in the unmanned vehicle is updated;
the result verification subunit is configured to obtain a test result, compare the vehicle operation information in the test result with the driver operation information in the expected result, compare the vehicle travel track information in the test result with the vehicle travel track information in the expected result, and determine whether the test is passed according to the comparison result.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of any one of claims 1 to 5.
12. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710431914.7A CN109032103B (en) | 2017-06-09 | 2017-06-09 | Method, device and equipment for testing unmanned vehicle and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710431914.7A CN109032103B (en) | 2017-06-09 | 2017-06-09 | Method, device and equipment for testing unmanned vehicle and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109032103A CN109032103A (en) | 2018-12-18 |
| CN109032103B true CN109032103B (en) | 2020-12-18 |
Family
ID=64628741
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710431914.7A Active CN109032103B (en) | 2017-06-09 | 2017-06-09 | Method, device and equipment for testing unmanned vehicle and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109032103B (en) |
Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113377033B (en) * | 2019-01-15 | 2024-03-22 | 北京百度网讯科技有限公司 | Data acquisition method, device, equipment and computer readable storage medium |
| CN109782737A (en) * | 2019-01-28 | 2019-05-21 | 平安科技（深圳）有限公司 | Unmanned vehicle analog detection method and system |
| CN111859597B (en) * | 2019-04-18 | 2024-04-05 | 北京京东乾石科技有限公司 | Evaluation method and system of automatic driving algorithm |
| CN110174274A (en) * | 2019-05-28 | 2019-08-27 | 初速度（苏州）科技有限公司 | A kind of modification method and device of intelligent driving algorithm |
| CN110497914B (en) * | 2019-08-26 | 2020-10-30 | 格物汽车科技(苏州)有限公司 | Method, apparatus and storage medium for developing a model of driver behavior for autonomous driving |
| CN110579216B (en) * | 2019-09-12 | 2022-02-18 | 阿波罗智能技术(北京)有限公司 | Test scene library construction method and device, electronic equipment and medium |
| CN110606093A (en) * | 2019-09-26 | 2019-12-24 | 中国第一汽车股份有限公司 | Vehicle performance evaluation method, device, equipment and storage medium |
| WO2021056556A1 (en) * | 2019-09-29 | 2021-04-01 | 驭势科技（北京）有限公司 | Vehicle-mounted autonomous driving test system and test method |
| CN112683544A (en) * | 2019-10-17 | 2021-04-20 | 北京百度网讯科技有限公司 | Unmanned vehicle driving capability verification method, device, equipment and storage medium |
| CN111142129A (en) * | 2019-11-26 | 2020-05-12 | 北京百度网讯科技有限公司 | Positioning system test method, device, equipment and storage medium |
| CN111292527A (en) * | 2020-01-22 | 2020-06-16 | 长安大学 | System and method for testing traffic capacity of special lane of unmanned vehicle |
| CN113361059A (en) * | 2020-03-05 | 2021-09-07 | 北京京东乾石科技有限公司 | Vehicle simulation scene generation method and related equipment |
| CN113742196B (en) * | 2020-05-27 | 2024-04-12 | 中车株洲电力机车研究所有限公司 | Testing method and device for intelligent driving software of locomotive |
| CN112289023B (en) * | 2020-10-09 | 2024-03-26 | 腾讯科技（深圳）有限公司 | Parking simulation test method and device for automatic driving and related equipment |
| CN112560258B (en) * | 2020-12-10 | 2023-02-21 | 中国第一汽车股份有限公司 | Test method, device, equipment and storage medium |
| CN112596500B (en) * | 2020-12-16 | 2022-01-28 | 清华大学苏州汽车研究院（相城） | Expected function safety analysis method for error/omission recognition of automatic driving vehicle |
| CN113183979B (en) * | 2021-05-26 | 2023-02-14 | 江苏盛海智能科技有限公司 | Drive-by-wire test method and system for unmanned vehicle |
| CN114720148B (en) * | 2022-03-23 | 2023-04-28 | 小米汽车科技有限公司 | Method, device, equipment and storage medium for determining vehicle perception capability |
| CN114741288A (en) * | 2022-03-31 | 2022-07-12 | 北京地平线机器人技术研发有限公司 | Data processing method, device, medium, system and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102350990A (en) * | 2011-06-29 | 2012-02-15 | 北京理工大学 | Comparison model for obstacle avoidance behaviors of vehicle under manned and unmanned conditions |
| CN103207090A (en) * | 2013-04-09 | 2013-07-17 | 北京理工大学 | Driverless vehicle environment simulation test system and test method |
| CN106153352A (en) * | 2016-07-04 | 2016-11-23 | 江苏大学 | A kind of automatic driving vehicle test and verification platform and method of testing thereof |
| CN106289797A (en) * | 2016-07-19 | 2017-01-04 | 百度在线网络技术（北京）有限公司 | For the method and apparatus testing automatic driving vehicle |
| CN106355880A (en) * | 2016-10-09 | 2017-01-25 | 东南大学 | Unmanned vehicle control parameter calibrating method for vehicle-following safety |
-
2017
- 2017-06-09 CN CN201710431914.7A patent/CN109032103B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102350990A (en) * | 2011-06-29 | 2012-02-15 | 北京理工大学 | Comparison model for obstacle avoidance behaviors of vehicle under manned and unmanned conditions |
| CN103207090A (en) * | 2013-04-09 | 2013-07-17 | 北京理工大学 | Driverless vehicle environment simulation test system and test method |
| CN106153352A (en) * | 2016-07-04 | 2016-11-23 | 江苏大学 | A kind of automatic driving vehicle test and verification platform and method of testing thereof |
| CN106289797A (en) * | 2016-07-19 | 2017-01-04 | 百度在线网络技术（北京）有限公司 | For the method and apparatus testing automatic driving vehicle |
| CN106355880A (en) * | 2016-10-09 | 2017-01-25 | 东南大学 | Unmanned vehicle control parameter calibrating method for vehicle-following safety |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109032103A (en) | 2018-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109032103B (en) | Method, device and equipment for testing unmanned vehicle and storage medium | |
| CN109032102B (en) | Unmanned vehicle testing method, device, equipment and storage medium | |
| CN108508881B (en) | Automatic driving control strategy adjusting method, device, equipment and storage medium | |
| US10636295B1 (en) | Method and device for creating traffic scenario with domain adaptation on virtual driving environment for testing, validating, and training autonomous vehicle | |
| JP6738932B2 (en) | System and method for training machine learning models located on a simulation platform | |
| US11548516B2 (en) | Data acquisition method, apparatus, device and computer-readable storage medium | |
| US11693409B2 (en) | Systems and methods for a scenario tagger for autonomous vehicles | |
| CN109141464B (en) | Navigation lane change prompting method and device | |
| CN107153363B (en) | Simulation test method, device, equipment and readable medium for unmanned vehicle | |
| US20200209874A1 (en) | Combined virtual and real environment for autonomous vehicle planning and control testing | |
| CN109116374B (en) | Method, device and equipment for determining distance of obstacle and storage medium | |
| CN110796007B (en) | Scene recognition method and computing device | |
| US10054945B2 (en) | Method for determining command delays of autonomous vehicles | |
| CN109255341B (en) | Method, device, equipment and medium for extracting obstacle perception error data | |
| CN110239562A (en) | The real-time perception adjustment based on surrounding vehicles behavior of automatic driving vehicle is adjusted with driving | |
| CN110377025A (en) | Sensor aggregation framework for automatic driving vehicle | |
| CN108027243A (en) | For operating the control error correction planing method of automatic driving vehicle | |
| JP6808775B2 (en) | Object tracking using multiple queues | |
| US20210009145A1 (en) | Automated factory testflow of processing unit with sensor integration for driving platform | |
| CN109085620A (en) | Automatic driving vehicle positions abnormal calibration method, apparatus, equipment and storage medium | |
| CN111062405A (en) | Method and device for training image recognition model and image recognition method and device | |
| CN114008551A (en) | Automated root cause analysis of faults in autonomous vehicles | |
| CN109118797A (en) | Information sharing method, device, equipment and storage medium | |
| US11142212B2 (en) | Safety-aware comparator for redundant subsystems in autonomous vehicles | |
| CN109827610B (en) | Method and device for verifying sensor fusion result |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |