CN111881519A - Automatic driving test method and device, computer equipment and storage medium - Google Patents

Automatic driving test method and device, computer equipment and storage medium Download PDF

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Publication number
CN111881519A
CN111881519A CN202010761494.0A CN202010761494A CN111881519A CN 111881519 A CN111881519 A CN 111881519A CN 202010761494 A CN202010761494 A CN 202010761494A CN 111881519 A CN111881519 A CN 111881519A
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automatic driving
test
daemon
cloud service
daemon process
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CN111881519B (en
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黄星尧
王亚亮
谭伟华
韩旭
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Guangzhou Jingqi Technology Co ltd
Guangzhou Weride Technology Co Ltd
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Guangzhou Jingqi Technology Co ltd
Guangzhou Weride Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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Abstract

The embodiment of the invention discloses a method and a device for testing automatic driving, computer equipment and a storage medium, wherein the method comprises the following steps: in the first cloud service, responding to a request for testing an automatic driving program, and setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface; selecting a machine for testing the automatic driving program from the second cloud service, wherein the machine is operated with a daemon process; in the second cloud service, reading parameters from the first cloud service by a daemon process; the daemon simulates an operation object and executes automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program. The real scene is used as a test scene, and the daemon process simulates an operation object to execute test operation, so that the test process of the whole automatic driving program is closer to the real automatic driving drive test process, and the obtained test result is more real and reliable.

Description

Automatic driving test method and device, computer equipment and storage medium
Technical Field
The embodiment of the invention relates to an automatic driving technology, in particular to a method and a device for testing automatic driving, computer equipment and a storage medium.
Background
Automatic driving is taken as the main direction of intelligent and networking development in the fields of global automobile and transportation travel at present, and has important value in future transportation.
In order to implement an automatic driving system of a vehicle, a test for an automatic driving program is a very important link. Currently, the most direct test method is to deploy an automatic driving program to a real vehicle for testing, which is called drive test. The drive test process typically requires some action on the vehicle by a security officer, such as starting the vehicle, manually taking over the vehicle if necessary, and the like. However, considering time, resources, safety and other factors, some simulation tests are usually performed in a virtual vehicle environment before the drive test to ensure that the significance of the drive test is maximized.
In the existing method, simulation test software applied to an automatic driving system often generates scene data and sensor data required by a test by configuring fixed parameters of the software, and because the type of test software is a pure virtual test environment, an obtained test result sometimes cannot well reflect the performance of an automatic driving program on a real vehicle.
Disclosure of Invention
The invention provides a method and a device for testing automatic driving, computer equipment and a storage medium, which are used for solving the problem that an automatic driving program is distorted in a test process under a pure virtual test environment.
In a first aspect, an embodiment of the present invention provides an automatic driving test method, including:
in a first cloud service, responding to a request for testing an automatic driving program, and setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface;
selecting a machine for testing the automatic driving program from a second cloud service, wherein a daemon process is operated in the machine;
in the second cloud service, the daemon reads the parameters from the first cloud service;
and the daemon simulates an operation object and executes automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program.
In a second aspect, an embodiment of the present invention further provides an automatic driving test apparatus, including:
the request response module is used for responding to a request for testing an automatic driving program in the first cloud service and setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface;
the machine determination module is used for selecting a machine for testing the automatic driving program from a second cloud service, and a daemon process is operated in the machine;
the parameter reading module is used for reading the parameter from the first cloud service by the daemon process in the second cloud service;
and the test module is used for simulating an operation object by the daemon process and executing automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a test method as described in the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the testing method according to the first aspect.
In the embodiment, in the first cloud service, in response to a request for testing an automatic driving program, setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface; selecting a machine for testing the automatic driving program from the second cloud service, wherein the machine is operated with a daemon process; in the second cloud service, reading parameters from the first cloud service by a daemon process; the daemon simulates an operation object and executes automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program. The test is a virtual test, scene data acquired by an actual vehicle in a real environment is used as a test scene of the test, the truth and the simulation degree of the test can be improved, and meanwhile, a daemon process simulates an operation object to execute automatic driving in the test scene, so that the test process of the whole automatic driving program is closer to the real automatic driving road test process, and the obtained test result is more real and reliable; furthermore, based on a more real and reliable test result, some problems existing in the automatic driving program in combination with real scene data and in the program can be tested in advance before the actual drive test, so that the automatic driving program can be adjusted in time according to the test result.
Drawings
FIG. 1 is a schematic diagram of an unmanned vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a test architecture according to an embodiment of the present invention;
fig. 3 is a flowchart of a test method for automatic driving according to an embodiment of the present invention;
fig. 4 is a flowchart of a test method for automatic driving according to a second embodiment of the present invention;
FIG. 5 is a schematic diagram of a test scenario site loop setup according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of an automatic driving test apparatus according to a third embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
It should be noted that: in the description of the embodiments of the present invention, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not intended to indicate or imply relative importance.
In order to further understand the technical solution of the present invention, the definition of autonomous driving and the hierarchy of autonomous driving technologies are analyzed as follows:
the automatic driving is a technology for enabling an automobile to have environment perception and path planning and autonomously realize vehicle control, namely human-simulated driving or automatic driving performed by controlling the automobile through an electronic technology.
According to the control degree of a vehicle system on vehicle control tasks, the automatic driving technology is divided into five levels of L0-L5, and the system mainly plays an auxiliary function in the levels of L1-L3; when level L4 is reached, the vehicle drive will be handed to the system in its entirety, whereas L4, L5 differ in specific scenarios and full-scene applications.
The L0 level is called the non-automation level and is defined as: the driver is responsible for performing dynamic driving tasks throughout, possibly assisted by vehicle system warnings or other intervention systems.
The L1 hierarchy is called the driver assistance hierarchy, and is defined as: in a specific driving mode, a single driving assistance system controls the transverse or longitudinal driving action of the vehicle by acquiring the driving environment information of the vehicle, but a driver needs to be responsible for operating other dynamic driving tasks.
The L2 hierarchy is referred to as the partial automation hierarchy, and its definition is: in a specific driving mode, a plurality of driving assistance systems simultaneously control the transverse or longitudinal driving actions of the vehicle by acquiring the driving environment information of the vehicle, but a driver needs to be responsible for operating other dynamic driving tasks.
The L3 hierarchy is called a conditional automation hierarchy, and its definition is: under a specific driving mode, the system is responsible for executing all dynamic driving tasks of the vehicle, and a driver needs to timely respond to an intervention request provided by the system when a special condition occurs.
The L4 level is called a highly automated level, and its definition is: in a particular driving mode, the system is responsible for performing all dynamic driving tasks of the vehicle, even if the driver fails to respond to the intervention request made by the system when a particular situation occurs.
The L5 level is called full automation level and its definition is: the system is responsible for completing all-weather all-road-condition dynamic driving tasks and can be managed by drivers.
Referring to fig. 1, there is shown an unmanned vehicle 100 to which embodiments of the automated driving test method, automated driving test apparatus, of embodiments of the present invention may be applied.
As shown in fig. 1, the unmanned vehicle 100 may include a driving Control device 101, a vehicle body bus 102, an ECU (Electronic Control Unit) 103, an ECU 104, an ECU105, a sensor 106, a sensor 107, a sensor 108, and an actuator 109, an actuator 110, and an actuator 111.
A driving control device (also referred to as an in-vehicle brain) 101 is responsible for overall intelligent control of the entire unmanned vehicle 100. The driving control device 101 may be a controller that is separately provided, such as a Programmable Logic Controller (PLC), a single chip microcomputer, an industrial controller, and the like; or the equipment consists of other electronic devices which have input/output ports and have the operation control function; but also a computer device installed with a vehicle driving control type application. The driving control device can analyze and process the data sent by each ECU and/or the data sent by each sensor received from the vehicle body bus 102, make a corresponding decision, and send an instruction corresponding to the decision to the vehicle body bus.
The body bus 102 may be a bus for connecting the driving control apparatus 101, the ECU 103, the ECU 104, the ECU105, the sensor 106, the sensor 107, the sensor 108, and other devices of the unmanned vehicle 100, which are not shown. Since the high performance and reliability of a CAN (Controller area network) bus are widely accepted, a vehicle body bus commonly used in a motor vehicle is a CAN bus. Of course, it is understood that the body bus may be other types of buses.
The vehicle body bus 102 may transmit the instruction sent by the driving control device 101 to the ECU 103, the ECU 104, and the ECU105, and the ECU 103, the ECU 104, and the ECU105 analyze and process the instruction and send the instruction to the corresponding execution device for execution.
Sensors 106, 107, 108 include, but are not limited to, lidar, cameras, accelerometers, gyroscopes, magnetometers, ultrasonic radar, and the like.
The laser radar is a device for detecting and measuring distance of an object by using laser as a sensor commonly used in the field of unmanned driving, and the sensor is internally provided with a rotating structure and can send millions of light pulses to the environment every second and output point cloud data.
Cameras are generally used to take pictures of the surroundings of an unmanned vehicle and record the scene in which the vehicle is traveling.
The accelerometer is also called a gravity sensor, and the magnitude and the direction of the acceleration in the axial direction are obtained by measuring the stress condition of the component in a certain axial direction.
The gyroscope is also called a ground sensor, the output data of the gyroscope is the magnitude and direction of a certain axial upper angular velocity, a common three-axis gyroscope is used, the working principle of the three-axis gyroscope is that an included angle between a vertical axis of a gyroscope rotor in a three-dimensional coordinate system and equipment is measured, the angular velocity is calculated, and the motion state of an object in a three-dimensional space is judged through the included angle and the angular velocity.
Magnetometers, also called geomagnetic and magnetic sensors, can be used for testing the intensity and direction of magnetic field and for locating the orientation of equipment.
The working principle of the ultrasonic radar is that the distance is measured and calculated by the time difference between the time when the ultrasonic wave is sent out by the ultrasonic wave transmitting device and the time when the ultrasonic wave is received by the receiver. The two common ultrasonic radars are a reversing radar which is arranged on a front bumper and a rear bumper of an automobile and is used for measuring front and rear obstacles of the automobile, and the other ultrasonic radar which is arranged on the side face of the automobile and is used for measuring the distance between side obstacles can be applied to parking garage position detection and high-speed transverse assistance.
It should be noted that the automated driving test method provided by the embodiment of the present invention may be executed by the driving control device 101, and accordingly, the automated driving test apparatus is generally disposed in the driving control device 101, and the automated driving test provided by the embodiment of the present invention refers to a virtual simulation test, where an automated driving program used for the test is also a program running in the virtual test, and the automated driving program may be executed by other modules in the driving control device 101 in cooperation.
It should be understood that the numbers of unmanned vehicles, driving control devices, body buses, ECUs, actuators, and sensors in fig. 1 are merely illustrative. There may be any number of unmanned vehicles, driving control devices, body buses, ECUs, and sensors, as desired for implementation.
In consideration of economic cost and time, before an unmanned vehicle runs on a road for testing, a large number of simulation tests are often required to be performed on an automatic driving program, and such simulation tests require that scene data are prepared in advance, for example, a security officer drives a real vehicle to run on the road (a road testing process) and records corresponding scene data at the same time.
The scene data includes data collected by various sensors equipped on the real vehicle, such as video data, acceleration, angular velocity, three-dimensional point cloud, and the like.
In order to manage and classify a large amount of scene data and facilitate future testing of the automatic driving program, a description file can be configured for each collected scene data, and specific information related to the scene data, such as scene type, sensor type, data type and the like, can be clearly written in the description file.
Because the scene data collected by the real vehicle in actual running is very limited, in order to enrich the test diversity and supplement more scene data for testing the automatic driving program, a random generator can be adopted to simulate the real scene data to generate virtual scene data, the virtual scene data and the real scene data are fused, and derivation and expansion are carried out on single scene data, or part of the scene data can be selected from the real scene data to be replaced by the random generator, or the random generator can be used to adjust part of the real scene data.
As shown in fig. 2, the test architecture provided by the embodiment of the present invention includes a first cloud service 210 and a second cloud service 220, and the first cloud service 210 and the second cloud service 220 are associated with each other.
The first cloud service 210 and the second cloud service 220 are cloud services, the cloud services are required services obtained through a network in an on-demand and easily-extensible manner, and the cloud services are mostly used for achieving purposes of data access, operation and the like.
The first cloud service 210 is generally deployed in one network, so that the client and the first cloud service 210 are in the same network, such as a public network, also called a public cloud, and are responsible for scheduling and generating parameters for testing, and the second cloud service 220 is generally deployed in another network, such as a local area network, also called a private cloud, and is responsible for executing testing.
The first cloud service 210 includes a control system 211, a database 212, and an interface 213.
The second cloud service 220 includes a machine 221, a machine 222, and a machine 223, where a daemon 2210 runs in the machine 221, a daemon 2220 runs in the machine 222, and a daemon 2230 runs in the machine 223. The daemon process is a process running in the background and is used for executing specific system tasks. The daemon process is typically started at system boot time and runs until the system is shut down.
The control system 211 is used for controlling the overall process of the test, and is responsible for scheduling of the machine, and performing control of various preparation steps before the test starts, etc., the database 212 is used for storing information that the first cloud service 210 and the second cloud service 220 need to use for interaction, and the interface 213 is used for an external device to access the first cloud service 210.
The interface 213 of the first cloud service 210 is opened to the daemon 2210, the daemon 2220 and the daemon 2230 in the second cloud service 220 to provide specific interaction data for the daemons, the database 212 in the first cloud service 210 is connected with the control system 211, the control system 211 can access data stored in the database 212, the states of the machines 221, 222 and 223 in the second cloud service 220 can be queried in the database 212, the database 212 in the first cloud service 210 is connected with the interface 213, the daemon 2210, the daemon 2220 and the daemon 2220 can access test detailed information in the database 212 through the interface 213, the daemon runs on each machine, interacts with data in the database 212, and executes specified programs and instructions on the machine for testing.
It should be understood that the number of interfaces of the first cloud service, the number of machines of the second cloud service in fig. 2 are merely illustrative. There may be any number of interfaces and machines, as the implementation requires.
Example one
Fig. 3 is a flowchart of an automatic driving test method according to an embodiment of the present invention, where the present embodiment is applicable to a situation where an actual vehicle executes an automatic driving test using automatic driving software, and is also applicable to a situation where a vehicle is simulated in a virtual test environment to perform a software test simulation of automatic driving, where the method may be executed by an automatic driving test apparatus, where the test apparatus may be implemented by software and/or hardware, and may be configured in a computer device, for example, a server, a personal computer, and other computing devices, and the method specifically includes the following steps:
step 301, in the first cloud service, setting parameters for testing the automatic driving program in response to a request for testing the automatic driving program.
In this embodiment, a user logs in a first cloud service at a client (e.g., a browser), and provides an automatic driving program to the first cloud service, requesting a test on the automatic driving program.
In order to improve the testing efficiency, the first cloud service can meet the requirements of a plurality of testing tasks, that is, the first cloud service can process a plurality of testing tasks in the same client, and can also process testing tasks submitted successively in a plurality of clients. Specifically, a user of the client may submit a request for a test task to the first cloud service through the network, the first cloud service may respond to the request submitted by the user, store the request in the queue, and the control system in the first cloud service may read the request for testing the autopilot from the queue according to the priority of the request, analyze the request, and perform a series of test preparation operations according to the analyzed result, for example, compile a source code of the autopilot, select a test module that is associated with the request, select an initialization mode, set parameters for testing the autopilot, and the like.
By applying the embodiment of the invention, technicians can drive real vehicles to run on the road surface, and collect video data, sensor data, walking routes and the like as scene data.
The scene data may be provided with labels indicating its main features, such as the area (including city, road, etc.) to which the road belongs, the type of the road (such as expressway, province, straight road, curve, etc.), the length of the road, the occurrence of the road (such as traffic accident, obstacle, traffic light, etc.), etc., so as to facilitate the user to select and match.
In this embodiment, the parameters of the test autopilot include scene data acquired when a real vehicle runs on a road surface, and the scene data of the test autopilot may be selected according to a user's requirement, for example, which specific scene data the user actively requires to test the autopilot, or for example, the user requires to select a type of the scene data, such as a highway, a city road, and the like, and for example, if the user does not have a requirement, the scene data is randomly selected, or the scene data with a higher test score is automatically selected, and the like.
Of course, the parameters of the test autopilot may include, in addition to the scene data collected when the real vehicle runs on the road surface, a location, a duration, a type of the sensor, a test frequency for the same scene data, and the like, which is not limited in this embodiment.
It should be noted that, when the first cloud service receives a large number of requests for testing the automatic driving program, if there is no priority limit, the requests stored in the queue are sequentially responded to, and this embodiment does not limit this.
Step 302, selecting a machine for testing the automatic driving program from the second cloud service.
In this embodiment, referring to fig. 2, the second cloud service 220 is configured to cooperate with the first cloud service 210 to test the auto-driving program, the second cloud service 220 has a plurality of machines (i.e., devices with computing capabilities) for testing the auto-driving program, when the second cloud service 220 is in a normal working state, a daemon process is already running in each machine, and the daemon processes 2210, 2220, 2230 can access the interface 213 exposed by the first cloud service 210 to obtain detailed information of the test, and then perform a test operation of the auto-driving program.
Generally, each machine in the second cloud service executes a test at the same time, and therefore, in this embodiment, the first cloud service maintains a real-time state of each machine in the second cloud service, and selects a machine for testing an appropriate auto-driving program according to the state to test the auto-driving program.
In a specific implementation, the state of each machine in the second cloud service can be queried locally in the first cloud service, where the state includes idle and occupied, the idle indicates that the machine does not execute the test, and the occupied indicates that the machine has executed the test.
If the state of traversing a certain machine is idle, the automatic driving program can be determined to be tested in the machine, and meanwhile, the state of the machine is modified from idle to occupied, so that other tests are prevented from being executed and conflicts are avoided.
Of course, when receiving the message sent by the machine and completing the test, the state of the machine may be modified from occupied to idle, and the resource is released to wait for the next test.
It should be noted that there are many ways to select a machine for testing an automatic driving program, and this embodiment is not limited to this. In addition to the above example method, the machines for testing the automatic driving program may be selected by, for example, recording the operating state of each machine in the second cloud service into a list, where the list may update the latest state of the machine in real time, and when a request for testing the automatic driving program is received, determining the machine for performing the test by reading the state of the machine in the list; or, a flag bit is set for each machine in the second cloud service, specifically, when the machine is in a working state, the flag bit is set to 1, when the machine is in a fault, the flag bit is set to N, and when the machine is in a normal idle state, the flag bit is set to 0.
Step 303, in the second cloud service, the daemon process reads the parameters from the first cloud service.
The first cloud service, upon selecting a machine, notifies a daemon process in the machine to perform a test on the autopilot, the daemon responding to the notification to request the autopilot and parameters suitable for testing the autopilot from the first cloud service.
In a specific implementation, referring to fig. 2, the first cloud service 210 has a database 212 in which parameters set for each test are stored, and an interface 213, and the daemon process in the second cloud service 220 can request parameters applied by the test thereof through the interface 213 exposed by the first cloud service 210.
After verifying the validity of the daemon process, the first cloud service 210 reads the parameters of the machine test automation program in which the daemon process is located in the database 212, and sends the parameters to the daemon process through the interface 213.
And step 304, simulating an operation object by the daemon process, and executing automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program.
In practical applications, in the process of testing a vehicle equipped with an automatic driving system on a road, in order to avoid an emergency, a safety staff is usually equipped, and the safety staff can execute a dynamic driving task on the vehicle, wherein the dynamic driving task includes controlling the speed, steering, lane changing, lights, horns and the like of the vehicle, and also includes actively avoiding obstacles, planning an actual driving path, making a decision to respond to a driving environment change, monitoring the driving environment in real time, preparing in advance and the like.
Different operation objects, i.e., objects having specific business operation capabilities, may be set for different business fields, and for tests in the field of automated driving, the operation objects are objects having vehicle driving manipulation capabilities, may be represented as security officers or remote operation terminals, and so on.
In this embodiment, in order to simulate a drive test process of a real vehicle, a virtual vehicle is constructed by simulating internal and external components of the real vehicle in a test platform, and the virtual vehicle can execute a virtual test.
Taking an operation object as an example of a security officer, the daemon acts as the security officer, and operates the test according to the parameters configured by the first cloud service, for example, when the test is started, the daemon simulates the security officer to start a virtual vehicle, performs a dynamic driving task on the virtual vehicle in scene data, can perform a destination change operation, a steering operation, a lane change operation, and the like by reading a place in the parameters, can keep the virtual vehicle running at a stable speed by reading the speed in the parameters, and can also start a take-over command and a command for starting an automatic driving mode in a specific scene data area, in short, the daemon simulates the security officer to perform automatic driving on the virtual vehicle in the scene data according to the parameters to test an automatic driving program.
Taking an operation object as a remote operation terminal as an example, a daemon process simulates the remote operation terminal to acquire parameters required by a test from a first cloud service, the daemon process issues a remote control command to a virtual vehicle according to different configuration information in the parameters and the simulated remote operation terminal so that the virtual vehicle executes automatic driving in scene data, and the daemon process simulates the remote operation terminal to remotely monitor and intervene in an automatic driving process of the virtual vehicle so as to test an automatic driving program.
In the embodiment, in the first cloud service, in response to a request for testing an automatic driving program, setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface; selecting a machine for testing the automatic driving program from the second cloud service, wherein the machine is operated with a daemon process; in the second cloud service, reading parameters from the first cloud service by a daemon process; the daemon simulates an operation object and executes automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program. The test is a virtual test, scene data acquired by an actual vehicle in a real environment is used as a test scene of the test, the truth and the simulation degree of the test can be improved, and meanwhile, a daemon process simulates an operation object to execute automatic driving in the test scene, so that the test process of the whole automatic driving program is closer to the real automatic driving road test process, and the obtained test result is more real and reliable; furthermore, based on a more real and reliable test result, some problems existing in the automatic driving program in combination with real scene data and in the program can be tested in advance before the actual drive test, so that the automatic driving program can be adjusted in time according to the test result.
Example two
Fig. 4 is a flowchart of an automated driving test method according to a second embodiment of the present invention, where the present embodiment supplements and refines the content of the automated driving test method based on the foregoing embodiment, and the method specifically includes the following steps:
step 401, in the first cloud service, in response to a request for testing an automatic driving program, setting parameters for testing the automatic driving program.
In this embodiment, the first cloud service may be a distributed computer group, may also be a large server, and may also be a network virtual machine, the first cloud service includes a control system, a database, and an external access interface, the first cloud service can meet the requirements of a plurality of clients on the test of the autopilot, users of different clients can upload the autopilot to be tested and actually acquired scene data to the first cloud service, and then, the control system and the database of the first cloud service are used to monitor and synchronously manage the test process of the autopilot. When the user lacks scene data for testing the automatic driving program, open-source scene data or a virtual scene generated by the random generator can be selected from the database of the first cloud service for testing, so that the problem that the robustness of the automatic driving program cannot be tested due to single test scene data of the user is solved.
In the first cloud service, the request for testing the automatic driving program can be read by accessing the queue for storing the request information in the database, so as to execute a specific test operation according to the category of the request, and it should be noted that before the automatic driving program test is entered, a preparation operation for testing needs to be executed according to the request in response. Wherein the preparation operation for executing the test comprises: setting parameters for testing an automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface; and selecting a test module, such as an obstacle avoidance module for testing the automatic driving program only, or a path planning module for testing the automatic driving program only.
Step 402, selecting a machine for testing the automatic driving program from the second cloud service.
The second cloud service can be a local cloud server, a virtual host, a local computing device or the like, and more than one machine for testing the automatic driving program is equipped in the second cloud service, and can participate in the test, and a daemon process is run in each machine.
After receiving the test request, the first cloud service establishes contact with the second cloud service, queries the state of the machine in the second cloud service, and when the machine in the idle state is detected, the first cloud service sends a scheduling instruction to inform the second cloud service, locks the machine and selects the machine to execute the test of the automatic driving program.
In the second cloud service, the daemon reads the parameters from the first cloud service.
In this embodiment, the second cloud service is associated with the first cloud service, and the daemon process in the second cloud service can access the database in the first cloud service to obtain parameters for testing, including the location, duration, frequency, sensor type, and the like of the automatic driving test.
Step 404, the daemon simulates the operation object to start the automatic driving mode so as to run the automatic driving program.
In this embodiment, the daemon process simulates an operation object to execute a start operation on the virtual vehicle, at this time, the daemon process monitors the running state of the virtual vehicle, and when the running state of the virtual vehicle is kept stable, the daemon process activates an automatic driving mode of the virtual vehicle so as to run an automatic driving program in real test scene data.
The embodiment is mainly suitable for automatic driving modes of L3 and L4 levels, an automatic driving program is responsible for executing all dynamic driving tasks of the vehicle, and when special conditions occur, the daemon can timely respond to an intervention request provided by the system. Under specific scene data, the present embodiment may also be applied to automatic driving test at L5 level.
Step 405, in the automatic driving mode, the daemon simulates an operation object input place so that the automatic driving program generates a test route which passes through the place and is located in the scene data.
When the automatic driving mode of the virtual vehicle is in an activated state, the daemon generates a set of a plurality of places according to scene data, the daemon simulates an operation object to set a place located at the head position in the set as a starting place, then the rest places in the set are set as destinations in sequence, and the daemon judges whether places which are not set as the destinations exist in the places or not; if so, reading the place which is not set as the destination by the daemon process, and setting the place which is not set as the destination; if not, the automatic driving program to be tested automatically plans the driving path according to the places, and generates a testing route which sequentially passes through the destinations from the starting place and finally returns to the starting place and is positioned in the scene data.
The daemon process simulates an input place of an operation object, and can simulate the operation of the operation object on a vehicle provided with an automatic driving system and a place to be passed by in a set driving route in the process of driving test more truly.
In a method for setting a destination, a daemon process extracts parameters from a database of a first cloud service, the parameters include a plurality of places generated for different scene data, the places can be arranged in a queue and the like in sequence, the daemon process reads the places in the queue in sequence, a test route is formed between every two places, the place at the head (namely the place where a virtual vehicle is initially located in the scene data) is the same as the place at the tail, so that a plurality of test routes can form a closed and trained test route, specifically, in an automatic driving mode, the daemon process reads data in the queue to judge whether a place which does not run (namely the place where the daemon process sets the destination) remains, if so, the daemon process reads the next place which does not run, simulates an operation object input place, and sets the place as the destination of the next automatic driving, so that the automatic driving program generates a test route from the current place to the next place which is not driven and is positioned in the scene data; if not, the daemon process determines to finish generating the test routes which are positioned in the scene data and circulate in all places, so that the test of the automatic driving program on the closed and trained test routes is finished once.
For example, as shown in fig. 5, the first place and the last place are the places a, the daemon drives the virtual vehicle to travel from the place a, the virtual vehicle continues to travel along the places B, C and D, when the virtual vehicle reaches the place E, the daemon determines that there are remaining un-traveled places F, G and H, the daemon reads the places F, G and H in turn, drives the virtual vehicle to continue to travel from the place E through the places F, G and H, at which time, the autopilot has generated a test route from the places a-B-place-C-place-D-place-E-place-F-place-G-place-H, until the virtual vehicle returns to the place a again, and the autopilot has generated a route from the places a-B-place-C-place-D-place-E-place-F-place-G-place-H-place-a-place And determining the test route of the point, namely determining to finish generating the test route which is positioned in the scene data of the test and circulates at all the places.
The first place and the last place are set to be the same (both starting places), so that the scenes for testing the automatic driving program can be connected end to end and can be played circularly, and the long-time test operation of the test platform is supported. Moreover, as the collected real scene data is limited, in order to meet the test requirement, the scene data of the test automatic driving program is played circularly, so that the robustness of the automatic driving program in single scene data can be tested, and the cost for collecting the scene data can be reduced.
Further, after completing the test of the automatic driving program on the closed and trained test route once, the daemon judges whether the test completion condition is met, thereby judging whether the test of the automatic driving program is completed. For example, the daemon process may determine whether to complete the test of the automatic driving program by reading a target duration and a target cycle number in preset parameters, that is, the duration of the test exceeds the target duration, or the number of tests in a closed and trained test route exceeds the target training number, that is, the test is considered to be completed, otherwise, the test is considered to be not completed.
If the testing of the autopilot program has been completed, a test result is generated for the autopilot program.
The test result can be presented in the form of a test report, the test report includes all log information of the virtual vehicle running the automatic driving program (time, driving speed, test route including location, number of times of mode switching operation, time point when the switching operation occurs, time ratio of manual driving mode to total test time length, etc.), performance information (execution efficiency of each module, system resource usage such as overall CPU, memory, I/O, etc.), stability report (whether system is abnormal or not), etc., and when the test is completed, the test report is returned to the submitter of the test request.
If the test autopilot is not completed, the states of all the sites are initialized, all the sites are set as the destinations again, and the next cycle of the test route is entered.
In the test route of the next cycle, the sensor type in the scene data may be changed to test the suitability of the sensor to the autopilot, for example, lidar, cameras, accelerometers and gyroscopes may be used in the last test, and lidar, ultrasonic radar, accelerometers, gyroscopes and magnetometers may be used in the next test cycle.
In the two implementation modes, the daemon judges whether the undriven place remains or not in the automatic driving mode, and the access frequency of the daemon to the place is used as a basis to judge the state of the test route generated by the automatic driving program.
In the automatic driving mode, the daemon process simulates the operation object to execute the starting operation so that the automatic driving program drives the virtual vehicle to run along the test route, step 406.
And after the automatic driving mode is confirmed to be started successfully, the daemon process simulates an operation object to issue a test starting instruction to the automatic driving program, and after the automatic driving program receives the test starting instruction, the virtual vehicle is driven to run along the test route.
Step 407, the daemon process determines that the automatic driving mode is abnormal.
The daemon process detects the current test state and compares the current test state with the state of a real vehicle when the real vehicle runs on the road surface and scene data is collected, so that whether the automatic driving mode is abnormal or not is determined, namely the automatic driving mode is consistent with the real vehicle, the automatic driving mode can be considered to be normal, the automatic driving mode is inconsistent with the real vehicle, and the automatic driving mode can be considered to be abnormal. The daemon process can record the detected states of the automatic driving mode in the test result, including the normal and abnormal test conditions of the automatic driving mode.
For example, the virtual vehicle does not run according to a preset rule in the test process, and the preset rule includes an obstacle avoidance rule, a vehicle running rule, a traffic regulation, a route planning rule, and the like; alternatively, the daemon process may determine that an abnormality occurs in the automatic driving mode by inquiring an output result in the automatic driving test process in real time, such as an abnormality in pose data of the virtual vehicle, an abnormality in output data of the sensor, and the like. The present embodiment does not limit the abnormality determination method of the automatic driving mode at all.
In one example, step 407 may include the following specific steps:
step 4071, the daemon determines the target route.
The target route is a route when the real vehicle runs on a road surface and scene data is collected, and generally, the target route is a preferred running route in the scene data.
The daemon process may search a database of the first cloud service for a route adapted to the tested scene data, and determine the route as a target route.
Step 4072, a first difference between the test route and the target route is calculated.
In this example, the test route may be compared with the target route, and the difference between the two may be calculated as the first difference to reflect the degree to which the test route deviates from the target route.
The test route and the target route can be both expressed by using position data of the virtual vehicle traveling in the scene data, taking an european space as an example, only a plane coordinate system (X-Y coordinate system) is considered, and assuming that a starting point of the virtual vehicle is a coordinate origin (0,0), the expression form of the position data is a plane coordinate (X, Y), X is a displacement value in an X-axis direction, Y is a displacement value in a Y-axis direction, and the forward direction of the virtual vehicle is specified in advance as an X-axis forward direction or a Y-axis forward direction in the test of the automatic driving program.
Therefore, a test route and a target route under the same scene data are selected, the position data of the test route and the position data of the target route are in one-to-one correspondence to each place in a test scene, then in each place, a first difference value of the position data of the test route and the position data of the target route in the X-axis direction and a second difference value of the position data of the test route and the position data of the target route in the Y-axis direction are counted, all the first difference values are summed and averaged to obtain a first average difference value, all the second difference values are summed and averaged to obtain a second average difference value, the average value of the first average difference value and the second average difference value is calculated, and the average value is used as a first difference between the test route and the. It should be noted that the present example does not limit the calculation manner of the first difference at all.
Step 4073, if the first difference exceeds a preset first threshold, determining that the automatic driving mode is abnormal.
In this example, a first threshold value, which represents a critical value of deviation, may be set in advance.
A first difference between the test route and the target route is compared to a first threshold.
If the first difference does not exceed the first threshold, the degree that the test route deviates from the target route is small, and whether the automatic driving mode is abnormal or not is continuously detected through the degree that the test route deviates from the target route within an allowable range.
If the first difference exceeds a preset first threshold value, the deviation degree of the test route from the target route is large, and the automatic driving mode can be determined to be abnormal if the deviation exceeds an allowable range.
In this embodiment, the first difference between the test route and the target route is calculated, and the first difference is compared with the preset first threshold, so that the difference between the test route and the target route can be visually seen from the value, and whether the state of the automatic driving mode is abnormal or normal can be judged through the first difference.
In addition to comparing the test route with the target route, it is also possible to determine that an abnormality occurs in the automatic driving mode of the automatic driving program by comparing the degree of overlap of the operations. For example, the steering operation in the automatic driving mode is frequent, and the steering operation at a plurality of places is different from the case of the drive test, and for example, the virtual vehicle stops at a certain place and turns around in the automatic driving mode, but the actual drive test does not occur.
And 408, responding to the abnormity, and the daemon process simulates the operation object to switch the automatic driving mode to the manual driving mode.
In a real road test scene, when an automatic driving mode of a real vehicle is abnormal, a security officer usually takes over the real vehicle by braking, turning a steering wheel and the like, stops the automatic driving mode, and starts manual driving to continuously execute a vehicle driving task to avoid accidents.
In this embodiment, the manual driving mode refers to a mode in which the daemon process intervenes in the test process of the automatic driving program. In order to make the process of testing the automatic driving program closer to the real drive test process, two test modes, namely an automatic driving mode and a manual driving mode, are configured in the automatic driving program, and the daemon process can simulate an operation object to switch the automatic driving mode to the manual driving mode after receiving a signal that the automatic driving mode is abnormal. The automatic driving mode and the manual driving mode are not affected with each other and can work independently, when the automatic driving mode is abnormal, the automatic driving program is switched to the manual driving mode, and the automatic driving program can also carry out autonomous error correction on the automatic driving mode within the period of time when the automatic driving program enters the manual driving mode in a test mode, so that the problem that the automatic driving program is easy to crash in a single test mode is avoided.
Information such as a time point, a location, and the like when the automatic driving mode is switched to the manual driving mode is recorded in the test result, for example, a first time point, a first scene, first location data, and the like when the switching occurs.
And step 409, in the manual driving mode, simulating the virtual vehicle driving by the operating object in the scene data by the daemon process until the automatic driving mode returns to normal.
In the manual driving mode, the daemon process simulates an operation object to perform a series of driving operations in the scene data, such as adjusting the traveling speed of the virtual vehicle, controlling the steering of the virtual vehicle, controlling the virtual vehicle to travel along a position in the middle of the lane, and the like. Until the automatic driving mode returns to normal, on one hand, the virtual vehicle is controlled to run continuously according to the mode, and the daemon process corrects the running route of the virtual vehicle towards the state of the real vehicle when the real vehicle runs on the road surface and collects scene data; on the other hand, the automatic driving mode performs autonomous repair to return to normal. Wherein the time ratio of the manual driving mode to the total duration of the test is recorded in the test result.
In an implementation manner of this embodiment, step 409 may include the following specific steps:
step 4091, in the manual driving mode, the daemon process simulates an operation object to drive the virtual vehicle with a trend target route as a target in the scene data.
The target route is the route of a real vehicle when the real vehicle runs on the road surface and scene data are collected. The test route generated in the automatic driving mode by the automatic driving program has larger deviation compared with the target route before the automatic driving program is switched to the manual driving mode. Therefore, in the manual driving mode, the daemon first acquires the position coordinates of each point in the target route, and then the simulation operation object sequentially reads the position coordinates of each point to drive the virtual vehicle to gradually approach the position coordinates to travel, so as to continue the test of the automatic driving program.
Step 4092, the daemon process reads a new test route generated by the automatic driving program.
During the period that the daemon process simulates an operation object to drive a virtual vehicle by taking a trend target route as a target in scene data, the automatic driving program generates a new test route in a manual driving mode, the new test route is suitable for driving in the automatic driving mode, and the daemon process reads the new test route to obtain position data of the new test route.
Step 4093, calculating a second difference between the new test route and the target route.
In this embodiment, taking the euclidean space as an example, only a plane coordinate system (X-Y coordinate system) is considered, and assuming that the starting point of the virtual vehicle is the origin of coordinates (0,0), the representation format of the position data is the plane coordinates (X, Y), X is the displacement value in the X-axis direction, Y is the displacement value in the Y-axis direction, and the forward direction of the virtual vehicle is specified in advance as the X-axis forward direction or the Y-axis forward direction in the test of the autopilot.
Calculating a second difference between the new test route and the target route may be performed in the following manner: and the position data of the new test route and the position data of the target route are in one-to-one correspondence to each place in the scene data, then in each place, the difference values of the position data of the new test route and the position data of the target route in the X-axis direction and the difference values of the position data of the new test route and the position data of the target route in the Y-axis direction are counted, the difference values in all the X-axis directions and the difference values in all the Y-axis directions are respectively summed and averaged to obtain the average difference value in the X-axis direction and the average difference value in the Y-axis direction, and the maximum value of the two average difference values is selected. It should be noted that the present embodiment does not limit the calculation method of the second difference at all.
Step 4094, if the second difference is smaller than a preset second threshold, determining that the automatic driving mode returns to normal.
In this embodiment, the maximum value of the difference between the positions of the position data of the new test route and the position data of the target route in the X-axis direction and the Y-axis direction of the planar coordinate system is selected as the second difference, the second difference is compared with the preset second threshold, and if the second difference is smaller than the preset second threshold, it is determined that the automatic driving mode returns to normal.
And when the automatic driving mode is determined to be recovered to the normal state, the daemon process starts the automatic driving mode, and the simulation operation object switches the manual driving mode to the automatic driving mode.
Information such as a time point, a location, and the like when the manual driving mode is switched to the automatic driving mode is recorded in the test result, for example, a second time point, a second scene, second location data, and the like when the switching occurs.
In the embodiment, the daemon simulates the operation object to switch the two modes of the automatic driving program, when the automatic driving mode is abnormal, the daemon process simulates an operation object to switch the automatic driving mode to the manual driving mode, so that the defect that the test is terminated due to the abnormal condition of a single mode can be overcome, the automatic driving program can run for a long time in the testing process, the stability of the automatic driving program in the testing process is enhanced, after the automatic driving mode is corrected by the automatic error correction module and returns to normal, the daemon process switches the manual driving mode to the automatic driving mode again, the switching process of the two modes is closer to the real vehicle drive test process, and finally, more real and diversified test results can be obtained, and the test results can provide more reliable and effective reference data for the automatic driving program.
EXAMPLE III
Fig. 6 is a schematic structural diagram of an automatic driving test device according to a third embodiment of the present invention, where the device may specifically include the following modules:
the request response module 601 is configured to, in the first cloud service, respond to a request for testing an automatic driving program, and set parameters for testing the automatic driving program, where the parameters include scene data acquired when a real vehicle runs on a road surface;
a machine determination module 602, configured to select, from a second cloud service, a machine in which a daemon process has been run, to test the autopilot;
a parameter reading module 603, configured to, in the second cloud service, read the parameter from the first cloud service by the daemon process;
the testing module 604 is configured to simulate an operation object by the daemon process, and execute automatic driving on the virtual vehicle in the scene data according to the parameter, so as to test the automatic driving program.
In one embodiment of the present invention, the machine determination module 602 includes:
the query submodule is used for querying the state of each machine in the second cloud service, and the state comprises idle state and occupied state;
the judgment submodule is used for determining that the automatic driving program is tested in a certain machine if the state of the machine is idle;
and the state modification submodule is used for modifying the state of the machine from idle to occupied.
In one embodiment of the present invention, the test module 604 comprises:
the automatic driving starting sub-module is used for the daemon process to simulate an operation object to start an automatic driving mode so as to run the automatic driving program;
the simulation test sub-module is used for enabling the daemon process to simulate an operation object to input the place in the automatic driving mode so that the automatic driving program generates a test route which passes through the place and is located in the scene data;
and the driving test submodule is used for simulating an operation object to execute starting operation in the automatic driving mode so that the automatic driving program drives the virtual vehicle to run along the test route.
In one embodiment of the invention, the simulation test sub-module comprises:
the site set acquisition unit is used for generating a set of a plurality of sites by the daemon process according to the scene data;
an origin setting unit, configured to, in the automatic driving mode, set, as an origin, a place located at a top position in the set by the daemon process simulation operation object;
a destination setting unit, configured to set, by the daemon process, the remaining places in the set as destinations in sequence;
a place determination unit for the daemon to determine whether there is a place that is not set as a destination among the places;
a destination determining unit, configured to, if yes, read, by the daemon, a place that is not set as a destination, and set, as the destination, the place that is not set as the destination;
and if not, enabling the automatic driving program to generate a test route which sequentially passes through the starting place, the destination and arrives at the starting place and is positioned in the scene data.
In one embodiment of the present invention, the simulation test sub-module further includes:
the test judgment unit is used for judging whether the automatic driving program is tested or not by the daemon process;
the test result generating unit is used for generating a test result for the automatic driving program if the automatic driving program is the driver's driving program;
and the circulating test unit is used for initializing the states of all the sites and returning to execute the daemon process to judge whether the sites which are not set as the destination exist in the sites if the sites are not set as the destination.
In an embodiment of the present invention, the test module 604 further comprises:
the abnormity determining submodule is used for determining that the automatic driving mode is abnormal by the daemon process;
the first mode switching submodule is used for responding to the abnormity, and the daemon process simulates an operation object to switch the automatic driving mode to a manual driving mode;
the manual driving test sub-module is used for simulating an operation object to drive a virtual vehicle in the scene data by the daemon process in the manual driving mode until the automatic driving mode returns to normal;
and the second mode switching submodule is used for responding to the normality and switching the manual driving mode to the automatic driving mode by the daemon process simulation operation object.
In one embodiment of the present invention, the abnormality determination submodule includes:
the target route determining unit is used for determining a target route by the daemon process, wherein the target route is a route obtained when the real vehicle runs on a road surface and the scene data is collected;
a difference calculation unit for calculating a first difference between the test route and the target route;
and the abnormality determining unit is used for determining that the automatic driving mode is abnormal if the first difference exceeds a preset first threshold value.
In one embodiment of the invention, the manual driving test submodule comprises:
the manual driving test unit is used for simulating that an operation object drives the virtual vehicle by taking the trend of the target route as a target in the scene data in the manual driving mode;
the test route reading unit is used for reading a new test route generated by the automatic driving program by the daemon process;
a difference calculation unit for calculating a second difference between the new test route and the target route;
and the normal state determining unit is used for determining that the automatic driving mode is recovered to be normal if the second difference is smaller than a preset second threshold value.
The automatic driving test device provided by the embodiment of the invention can execute the automatic driving test method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 7 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. As shown in fig. 7, the computer apparatus includes a processor 700, a memory 701, a communication module 702, an input device 703, and an output device 704; the number of the processors 700 in the computer device may be one or more, and one processor 700 is taken as an example in fig. 7; the processor 700, the memory 701, the communication module 702, the input device 703 and the output device 704 in the computer apparatus may be connected by a bus or other means, and fig. 7 illustrates an example of connection by a bus.
The memory 701 may be used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as modules corresponding to the test method for automated driving in the present embodiment (for example, a request response module 601, a machine determination module 602, a parameter reading module 603, and a test module 604 in the test apparatus for automated driving shown in fig. 6). The processor 700 executes various functional applications and data processing of the computer device by executing software programs, instructions and modules stored in the memory 701, that is, the automatic driving test method described above is implemented.
The memory 701 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 701 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 701 may further include memory located remotely from processor 700, which may be connected to a computer device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
And the communication module 702 is used for establishing connection with the display screen and realizing data interaction with the display screen.
The input device 703 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of a computer apparatus, a camera for acquiring images and a sound pickup apparatus for acquiring audio data.
The output device 704 may include an audio device such as a speaker.
It should be noted that the specific composition of the input device 703 and the output device 704 may be set according to actual situations.
The processor 700 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 701, that is, implements the above-described connection node control method of the electronic whiteboard.
The computer device provided by the embodiment of the invention can execute the automatic driving test method provided by any embodiment of the invention, and has corresponding functions and beneficial effects.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for testing automatic driving, and the method includes:
in a first cloud service, responding to a request for testing an automatic driving program, and setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface;
selecting a machine for testing the automatic driving program from a second cloud service, wherein a daemon process is operated in the machine;
in the second cloud service, the daemon reads the parameters from the first cloud service;
and the daemon simulates an operation object and executes automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program.
Of course, the computer program of the computer-readable storage medium provided in the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the test method for automatic driving provided in any embodiments of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the automatic driving test apparatus, the included units and modules are only divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (11)

1. A method for testing autonomous driving, comprising:
in a first cloud service, responding to a request for testing an automatic driving program, and setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface;
selecting a machine for testing the automatic driving program from a second cloud service, wherein a daemon process is operated in the machine;
in the second cloud service, the daemon reads the parameters from the first cloud service;
and the daemon simulates an operation object and executes automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program.
2. The method of claim 1, wherein selecting, from a second cloud service, a machine to test the autopilot, the machine having a daemon running thereon, comprises:
inquiring the state of each machine in the second cloud service, wherein the state comprises idle state and occupied state;
if the state of a certain machine is idle, determining to test the automatic driving program in the machine;
modifying a state of the machine from idle to occupied.
3. The method of claim 1, wherein the parameters further include a location;
the daemon process simulates an operation object, and executes automatic driving on the virtual vehicle in the scene data according to the parameters to test the automatic driving program, and the method comprises the following steps:
the daemon process simulates an operation object to start an automatic driving mode so as to run the automatic driving program;
in the automatic driving mode, the daemon process simulates an operation object to input the place so that the automatic driving program generates a test route which passes through the place and is located in the scene data;
in the automatic driving mode, the daemon process simulates an operation object to execute starting operation so that the automatic driving program drives the virtual vehicle to run along the test route.
4. The method of claim 3, wherein in the autopilot mode, the daemon simulates an operator entering the location to cause the autopilot to generate a test route through the location and within the scene data, comprising:
the daemon process generates a set of a plurality of places according to the scene data;
in the automatic driving mode, the daemon process simulates an operation object to set a place at the head position in the set as a starting place;
the daemon process sequentially sets the rest places in the set as destinations;
the daemon process judges whether a place which is not set as a destination exists in the places;
if so, the daemon process reads a place which is not set as a destination, and sets the place which is not set as the destination;
if not, enabling the automatic driving program to generate a test route which sequentially passes through the destination from the starting place to the starting place and is located in the scene data.
5. The method of claim 4, wherein in the autopilot mode, the daemon simulates an operator entering the location to cause the autopilot to generate a test route through the location and within the scene data, further comprising:
the daemon judges whether the automatic driving program is tested or not;
if so, generating a test result for the automatic driving program;
if not, initializing the states of all the places, and returning to execute the daemon process to judge whether the places which are not set as the destination exist in the places.
6. The method of claim 3, wherein the daemon performs autopilot on a virtual vehicle in the context data according to the parameters to test the autopilot, further comprising:
the daemon process determines that the automatic driving mode is abnormal;
in response to the abnormality, the daemon process simulates an operation object to switch the automatic driving mode to a manual driving mode;
in the manual driving mode, the daemon process simulates an operation object to drive a virtual vehicle in the scene data until the automatic driving mode returns to normal;
in response to the normality, the daemon process simulates an operation object to switch the manual driving mode to the automatic driving mode.
7. The method of claim 6, wherein the daemon determines that the autopilot mode is abnormal, comprising:
the daemon process determines a target route, wherein the target route is a route when the real vehicle runs on a road surface and the scene data is collected;
calculating a first difference between the test route and the target route;
and if the first difference exceeds a preset first threshold value, determining that the automatic driving mode is abnormal.
8. The method of claim 7, wherein in the manual driving mode, the daemon process simulates an operator object driving a virtual vehicle in the scene data until the automatic driving mode returns to normal, comprising:
in the manual driving mode, the daemon process simulates an operation object to drive the virtual vehicle by taking the trend of the target route as a target in the scene data;
the daemon process reads a new test route generated by the automatic driving program;
calculating a second difference between the new test route and the target route;
and if the second difference is smaller than a preset second threshold value, determining that the automatic driving mode is recovered to be normal.
9. An autopilot test apparatus, comprising:
the request response module is used for responding to a request for testing an automatic driving program in the first cloud service and setting parameters for testing the automatic driving program, wherein the parameters comprise scene data acquired when a real vehicle runs on a road surface;
the machine determination module is used for selecting a machine for testing the automatic driving program from a second cloud service, and a daemon process is operated in the machine;
the parameter reading module is used for reading the parameter from the first cloud service by the daemon process in the second cloud service;
and the test module is used for simulating an operation object by the daemon process and executing automatic driving on the virtual vehicle in the scene data according to the parameters so as to test the automatic driving program.
10. A computer device, characterized in that the computer device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a test method as claimed in any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the testing method according to any one of claims 1-8.
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