CN115879323A - Automatic driving simulation test method, electronic device and computer readable storage medium - Google Patents
Automatic driving simulation test method, electronic device and computer readable storage medium Download PDFInfo
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Abstract
The application provides an automatic driving simulation test method, electronic equipment and a computer readable storage medium, which realize large-scale cloud simulation under the condition of not using containerization technology and Kubernets, and the specific method comprises the following steps: when the computational complexity of the simulation test task is too high and the simulation frequency which can be realized by a single server cannot meet the requirement, the simulation test task is disassembled into a plurality of servers to cooperatively operate, and the output result is synchronously transmitted to the same automatic driving system. Through the improvement, the cloud simulation achieves the utilization rate of server resources close to 100%, and the hardware-in-loop simulation and the whole vehicle-in-loop simulation can be supported no matter how complex the test tasks are.
Description
Technical Field
The present disclosure relates to the field of automatic driving technologies, and in particular, to an automatic driving simulation test method, an electronic device, and a computer-readable storage medium.
Background
At present, most of cloud simulation for an auxiliary driving system or an automatic driving system adopts a container-based scheme, each instance is operated in one container, one server can operate a plurality of instances, and the containers are scheduled through Kubernets, so that operations such as arrangement are realized. However, the above operations cause the use of resources such as CPU, and further reduce the testing efficiency.
Disclosure of Invention
An object of an embodiment of the present application is to provide an automatic driving simulation testing method, an electronic device, and a computer-readable storage medium, so as to solve the problems in the prior art that a containerization technique and kubernets are applied to a simulation test of an auxiliary driving system and an automatic driving system, which results in difficulty in disassembling a complex simulation task, additional overhead, and reduction in the utilization rate of server resources.
The embodiment of the application provides an automatic driving simulation test method, which comprises the following steps:
acquiring a simulation test task of a target automatic driving system, and determining the simulation frequency and the characteristic information of the simulation test task, wherein the characteristic information is used for representing the complexity of the simulation test task;
dividing the simulation test task into a plurality of simulation test subtasks according to the characteristic information of the simulation test task;
and synchronously distributing each simulation test subtask and the vehicle attitude corresponding to the target automatic driving system in the simulation test tasks to a plurality of servers, so that the plurality of servers can synchronously execute the distributed simulation test subtasks according to the simulation frequency, and carry out simulation test according to the vehicle attitude of the target automatic driving system.
In the technical scheme, large-scale cloud simulation is realized without using a containerization technology and Kubernets, and the specific method comprises the following steps: when the computational complexity of the simulation test task is too high and the simulation frequency which can be realized by a single server cannot meet the requirement, the simulation test task is disassembled into a plurality of servers to cooperatively operate, and the output result is synchronously transmitted to the same automatic driving system. Through the improvement, the cloud simulation achieves the utilization rate of the server resources close to 100%, and the support for hardware-in-loop simulation and whole-vehicle-in-loop simulation can be achieved no matter how complex the test task is.
In some optional embodiments, the characteristic information of the simulation test task includes a simulation complexity of the test vehicle and/or a simulation complexity of the test scenario.
In the above technical solution, the characteristic information of the simulation test task includes the simulation complexity of the test vehicle, for example, when the simulation test task is split, the corresponding simulation test subtask may be split according to each sensor or positioning device of the test vehicle. The feature information of the simulation test task also includes the simulation complexity of the test scenario, for example, when the simulation test task is split, the corresponding simulation test subtasks can be split according to NPC vehicles and traffic participants (such as traffic lights) in the test scenario.
In some optional embodiments, the obtaining a simulation test task of the target automatic driving system and the determining the characteristic information of the simulation test task include:
acquiring the information of an automatic driving system of a test vehicle in a simulation test task, and determining a vehicle dynamics model solver corresponding to the automatic driving system of the test vehicle;
and determining the simulation complexity of the test vehicle according to a vehicle dynamics model solver.
In the above technical solution, the simulation complexity of the test vehicle is related to the vehicle dynamics model solver, and the relationship mapping table of the simulation complexity of the test vehicle corresponding to different vehicle dynamics model solvers may be stored in advance, and when the simulation test task is actually disassembled, the query is performed according to the relationship mapping table to obtain the simulation complexity of the test vehicle.
In some optional embodiments, obtaining a simulation test task of the target automatic driving system, and determining characteristic information of the simulation test task includes:
acquiring sensing module information of a test vehicle in a simulation test task, and determining the type and the number of sensing modules;
and determining the simulation complexity of the test vehicle according to the type and the number of the sensing modules.
In the technical scheme, the sensing module comprises the sensor and the positioning equipment, and the simulation complexity of the test vehicle can be determined according to the type and the number of the sensor and the positioning equipment, wherein the more the types and the number of the sensor and the positioning equipment are, the higher the simulation complexity of the test vehicle is, and when the simulation test task is disassembled, the simulation test subtasks corresponding to the sensing modules in the set number of the same type can be distributed to the same server.
In some optional embodiments, obtaining a simulation test task of the target automatic driving system, and determining characteristic information of the simulation test task includes:
acquiring scene elements of a test scene in a simulation test task, and determining action types corresponding to the scene elements;
and determining the simulation complexity of the test scene according to the action types corresponding to the scene elements.
In the above technical solution, NPC vehicles other than the test vehicle, traffic participants, maps, and the like are collectively referred to as scene elements in the test scene, and therefore, according to the action types corresponding to the scene elements, the simulation complexity of the test scene can be determined.
In some optional embodiments, dividing the simulation test task into a plurality of simulation test subtasks according to the feature information of the simulation test task includes:
and dividing simulation test subtasks corresponding to the perception modules of the same type in the simulation test task into a server according to the types of the different perception modules in the simulation test task.
In some optional embodiments, after synchronously distributing each simulation test subtask and the vehicle attitude corresponding to the target automatic driving system in the simulation test task to a plurality of servers, the method further includes:
and setting simulation frequencies of different sensing modules, sending corresponding simulation frequencies to a plurality of servers, and ensuring that the plurality of servers are in a time synchronization state.
In some optional embodiments, the plurality of servers may synchronously execute the distributed simulation test subtasks according to the simulation frequency, and perform the simulation test according to the vehicle posture of the target automatic driving system, including:
after each server executes the distributed simulation test subtasks, obtaining perception data of at least one perception module;
respectively sending perception data obtained by simulating each server according to the simulation frequency of different perception modules to corresponding input ends of an automatic driving system of a test vehicle;
and updating the attitude of the test vehicle according to the control signal output by the automatic driving system.
In some optional embodiments, the method is applied to hardware-in-loop simulation or vehicle-in-loop simulation, and sends simulation frequencies to a plurality of servers, and comprises the following steps:
and setting corresponding simulation frequency according to the working frequency of a sensing module of the automatic driving system of the test vehicle, determining a server capable of realizing the corresponding simulation frequency, and sending the corresponding simulation frequency to a plurality of servers.
In the technical scheme, when the computational complexity of the simulation test task is too high and the simulation frequency which can be realized by a single server cannot meet the requirement, the test task is disassembled to a plurality of servers to cooperatively operate, the output result is synchronously transmitted to the same automatic driving system, and the true frequency is set as the working frequency of a sensor of the automatic driving system, so that the support of hardware-in-loop simulation and whole-vehicle-in-loop simulation can be realized.
An electronic device provided in an embodiment of the present application includes: a processor and a memory, the memory storing machine-readable instructions executable by the processor, the machine-readable instructions, when executed by the processor, performing a method as in any above.
A computer-readable storage medium is provided in an embodiment of the present application, and has a computer program stored thereon, where the computer program is executed by a processor to perform the method described in any one of the above.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart illustrating steps of an automatic driving simulation test method according to an embodiment of the present disclosure;
FIG. 2 is a functional block diagram of an automatic driving simulation test system according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an autopilot simulation test system provided by an embodiment of the application;
FIG. 4 is a schematic diagram of an autopilot simulation test system according to another embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Icon: 100-user interface, 101-simulator cluster, 1011-simulator, 102-data bus, 103-adapter cluster, 1031-signal converter, 1032-model solver, 104-system under test, 105-simulation task scheduler, 21-processor, 22-memory, 23-communication interface, 24-communication bus.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
First, the following terms mentioned in one or more embodiments of the present application are annotated:
cloud simulation: and the large-scale simulation test program is performed in the simulator cluster in a dynamic scheduling and deploying mode. The performance indexes of the cloud simulation mainly comprise: flexibility, e.g., time consumption for capacity expansion, capacity reduction; server resource utilization, such as CPU utilization, GPU utilization, storage device utilization, and the like.
Example (c): cloud emulation consists of a series of instances, each running on a server. A test task or a portion of the test task is performed. The number of the instances can be increased or reduced through operations such as capacity expansion and capacity reduction, so that a large number of test tasks can be simultaneously executed on a large number of instances through cloud simulation according to needs, and the test efficiency is improved.
The system to be tested: the test object of the cloud simulation program comprises an automatic driving system and an auxiliary driving system.
Simulation frequency: the cloud simulation program generates an update frequency of the simulation data for the autopilot system. For example, the simulation frequency of a vehicle dynamics model is typically on the order of 100 Hz.
A perception module: the electronic or optical device which is installed in the automatic driving system and has the functions of perception and/or positioning and the like can output perception information such as images, videos, two-dimensional or three-dimensional point clouds, traffic participant information lists and the like of a road environment and/or positioning information such as the position, the speed, the posture, the acceleration and the like of a carrier where the device is located. Typical real devices include cameras, millimeter wave radars, lidar, global Navigation Satellite Systems (GNSS), inertial Measurement Units (IMU), and the like.
Operating frequency of the sensing module: a frame rate of the output data from the sensor or the positioning device. For example, the operating frequency of a camera is typically in the range of 20-60 Hz.
Closed loop simulation: a simulation method includes generating real and/or virtual sensor data and positioning device data according to data such as a map, a test scene, a test environment, a state of a tested vehicle, a state of an NPC vehicle and the like, information such as data acquired in an actual traffic environment and the like, sending the data to a tested object, sending the data output by the tested object to a simulated or real vehicle to obtain position and posture changes of the vehicle, generating new data of a real or virtual sensor and/or positioning device according to the changes, repeating the steps in such a cycle, and continuously performing qualitative and quantitative evaluation on the output of the tested object and the state of the vehicle.
Model-in-loop simulation: a closed loop simulation method whose test objects are typically algorithms and/or models of software assisted driving or autonomous driving systems.
The software is simulated in a ring: a closed loop simulation method is disclosed, the object to be tested is usually software of a software-assisted driving or automatic driving system.
Processor-in-loop simulation: a closed loop simulation method whose test object is typically software on a target platform (typically a domain controller) for a software assisted driving or autonomous driving system.
Hardware-in-the-loop simulation: a closed loop simulation method is disclosed, the object to be tested is usually a domain controller, and the simulation frequency is equal to the working frequency of the device installed on the automatic driving system.
The whole vehicle is simulated in a ring: the tested object is usually a vehicle, and the simulation frequency is equal to the working frequency of the device installed on the automatic driving system.
At present, most of cloud simulation for an auxiliary driving system or an automatic driving system adopts a container-based scheme, each instance is operated in one container, one server can operate a plurality of instances, and the containers are scheduled through Kubernets, so that operations such as arrangement are realized. The key technical indexes of the cloud simulation comprise the overhead of the system and the maximum simulation frequency. The overhead of the system comprises the utilization rate of resources such as a CPU (central processing unit) and storage equipment of the server; the overhead of the system determines the cost of cloud simulation: the smaller the overhead, the lower the cost. When the maximum simulation frequency is not less than the working frequency of devices of the automatic driving system, the cloud simulation can realize hardware-in-loop simulation and whole vehicle-in-loop simulation; otherwise, cloud simulation can only realize software in-loop simulation and model in-loop simulation.
The containerization technology and the Kubernetes are suitable for the application scenarios of cloud computing with low computing complexity and particularly large concurrency number. However, the characteristics of the simulation test application of the driver assistance system and the automatic driving system are exactly opposite to the above: the computational complexity is particularly high and the number of concurrencies is not particularly large. The computational complexity of the simulation test is proportional to the number of devices, and the number of devices of the vehicle increases rapidly with the increase of the degree of intellectualization. For example, high-grade autonomous vehicles typically have over 10 cameras and millimeter-wave radars, as well as multiple lidar, which causes the simulation frequency of test task instances running on a single server to be typically much lower than the operating frequency of the devices, severely reducing test efficiency, and failing to support hardware-on-loop simulation and whole-vehicle-on-loop simulation. In addition, the containerization technique also causes the use overhead of resources such as a CPU, and the like, and further reduces the test efficiency. These defects can be summarized as follows:
1. complex simulation tasks are difficult to disassemble, and a test case cannot be cooperatively executed by a plurality of servers, so that the simulation frequency is usually lower than the working frequency of a device, and the requirements of hardware-in-loop simulation and whole-vehicle-in-loop simulation cannot be met;
2. causing additional overhead and reducing the utilization of server resources. For example, each container has its own file system, reducing the utilization of storage devices; the containers are mutually isolated, the hardware resources are virtualized, and the utilization rate of the CPU and the GPU is reduced.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of an automatic driving simulation testing method according to an embodiment of the present application, which specifically includes:
s1, acquiring a simulation test task of a target automatic driving system, and determining simulation frequency and characteristic information of the simulation test task, wherein the characteristic information is used for representing the complexity of the simulation test task;
s2, dividing the simulation test task into a plurality of simulation test subtasks according to the characteristic information of the simulation test task;
and S3, synchronously distributing each simulation test subtask and the vehicle attitude corresponding to the target automatic driving system in the simulation test tasks to a plurality of servers, so that the plurality of servers can synchronously execute the distributed simulation test subtasks according to the simulation frequency, and perform simulation test according to the vehicle attitude of the target automatic driving system.
In the embodiment of the application, large-scale cloud simulation is realized without using a containerization technology and Kubernets, and the specific method comprises the following steps: when the computational complexity of the simulation test task is too high and the simulation frequency which can be realized by a single server cannot meet the requirements, the simulation test task is disassembled into a plurality of servers to cooperatively operate, and the output result is synchronously transmitted to the same automatic driving system. Through the improvement, the cloud simulation achieves the utilization rate of the server resources close to 100%, and the support for hardware-in-loop simulation and whole-vehicle-in-loop simulation can be achieved no matter how complex the test task is.
In some optional embodiments, the characteristic information of the simulation test task includes a simulation complexity of the test vehicle and/or a simulation complexity of the test scenario. In the embodiment of the application, the feature information of the simulation test task includes the simulation complexity of the test vehicle, for example, when the simulation test task is split, the corresponding simulation test subtask can be split according to each sensor or positioning device of the test vehicle. The feature information of the simulation test task also includes the simulation complexity of the test scenario, for example, when the simulation test task is split, the corresponding simulation test subtasks can be split respectively according to the NPC vehicle and the traffic participant (such as traffic lights) in the test scenario.
In some optional embodiments, obtaining a simulation test task of the target automatic driving system, and determining characteristic information of the simulation test task includes: acquiring the information of an automatic driving system of a test vehicle in a simulation test task, and determining a vehicle dynamics model solver corresponding to the automatic driving system of the test vehicle; and determining the simulation complexity of the test vehicle according to a vehicle dynamics model solver.
In the embodiment of the application, the simulation complexity of the test vehicle is related to the vehicle dynamics model solver, and the relationship mapping table of the simulation complexity of the test vehicle corresponding to different vehicle dynamics model solvers can be stored in advance, and when the simulation test task is actually disassembled, the query is performed according to the relationship mapping table to obtain the simulation complexity of the test vehicle.
In some optional embodiments, obtaining a simulation test task of the target automatic driving system, and determining characteristic information of the simulation test task includes: acquiring sensing module information of a test vehicle in a simulation test task, and determining the type and the number of sensing modules; and determining the simulation complexity of the test vehicle according to the type and the number of the sensing modules.
In the embodiment of the application, the sensing module comprises the sensor and the positioning equipment, and the simulation complexity of the test vehicle can be determined according to the types and the number of the sensor and the positioning equipment, wherein the more the types and the larger the number of the sensor and the positioning equipment are, the higher the simulation complexity of the test vehicle is, and when the simulation test task is disassembled, the simulation test subtasks corresponding to the sensing modules in the set number of the same type can be distributed to the same server.
In some optional embodiments, the obtaining a simulation test task of the target automatic driving system and the determining the characteristic information of the simulation test task include: acquiring scene elements of a test scene in a simulation test task, and determining action types corresponding to the scene elements; and determining the simulation complexity of the test scene according to the action types corresponding to the scene elements.
In the embodiment of the application, NPC vehicles, traffic participants, maps and the like except the test vehicle are collectively referred to as scene elements in the test scene, so that the simulation complexity of the test scene can be determined according to the action types corresponding to the scene elements.
It should be noted that the elements in the simulation test are mainly divided into two parts, namely, the elements for testing the vehicle and scene elements; the scene elements also cover: static environment elements, dynamic environment elements, traffic participant elements, meteorological elements, and the like. Testing the self factors of the vehicle: the method is used for testing the basic attribute, the position information, the motion state information and the driving task information of the vehicle. Static environment elements: refers to an object in a stationary state in a traffic environment, comprising: roads, traffic facilities, surrounding landscapes, obstacles, and the like. Dynamic environment elements: refers to an element in a traffic environment which is in dynamic change, and comprises the following components: and dynamically indicating facility and communication environment information. Traffic participant elements: the object information influencing the decision planning of the vehicle in the automatic driving test scene comprises the following information: vehicles, pedestrians, and animals. Meteorological elements: including information such as ambient temperature, lighting conditions, and weather conditions in the driving scene.
The autopilot system development is subject to three phases: a concept stage, a system development stage and a test stage; with the gradual progress of the system development process, the abstraction level requirement of the test scenarios is continuously reduced, but the number requirement of the test scenarios is continuously increased. The logical scenario can be transformed by combining a structured functional scenario with a parameter range, which can be defined by a data-driven approach. Each logical scenario may be converted to a specific scenario by selecting a specific value from a range of parameters.
Wherein, the functional scene is as follows: describing the entities in the scene area and the relationship among the entities through the most abstract operation scene of the semantic description, namely, through language scene symbols; the functional scene is used for project definition, risk analysis and risk assessment in the concept phase; in the testing process, the functional scenario is often required to be converted into a logic scenario and into a data format that can be used in a corresponding simulation environment.
Logical scenarios: expressing the relationship between the entity characteristics and the entities by defining the parameter range of the variable in the state space; the logic scene is a further detailed description of the function scene based on the state space variable and is used for generating requirements in a project development phase; for each logical scenario with a continuous range of values, any number of specific scenarios can be derived.
The specific scene is as follows: the method comprises the steps of specifically describing the relationship between entities by determining the specific value of each parameter in a state space, and describing a test scene in detail by the state space; specific scenes can be directly converted into test cases; to convert a specific scenario into a test case, the expected performance of the tested object needs to be increased, and the requirements on related test facilities need to be met.
Classifying according to the data source of the test scene: natural driving scenes, dangerous working condition scenes, standard regulation scenes and parameter recombination scenes. Wherein, natural driving scene: the data source is the most basic data source in the real natural driving state scene of the automobile; the system comprises all-round information such as human-vehicle-environment-task and the like of an automatic driving automobile; the natural driving scene can provide multidimensional information such as vehicle data, driver behaviors and road environment, and is a sufficient test scene for proving the effectiveness of automatic driving. Dangerous working condition scene: the data mainly come from a traffic accident database and are the key for verifying the safety and reliability of the automatic driving control strategy; dangerous working condition scenes mainly cover three major scenes of severe weather environment, complex road traffic and typical traffic accidents, and are necessary test scenes for proving the effectiveness of automatic driving. Standard regulatory scenarios: the data mainly come from the existing standards, evaluation rules and the like, such as multiple standards and evaluation rules of ISO, NHTSA, E-NCAP, C-NCAP and the like, and the existing automatic driving function is tested and specified; the standard and regulation test scenario is the basic test scenario that the autopilot function must meet during the development and certification phases. Parameter recombination scenario: the data comes from the existing scene database resources, and scenes of corresponding types are randomly generated or automatically recombined by carrying out parameterization setting on the existing simulation scenes; the parameter recombination scene expands the boundary of the parameter recombination scene by carrying out different permutation and combination and traversal values on static elements, dynamic elements, behavioral elements of a driver and the like; the blind area for testing the automatic driving function is effectively covered, and the test scene is an effective supplementary test scene for unknown working conditions.
In some optional embodiments, dividing the simulation test task into a plurality of simulation test subtasks according to the feature information of the simulation test task includes: and dividing simulation test subtasks corresponding to the perception modules of the same type in the simulation test task into a server according to the types of the different perception modules in the simulation test task.
In some optional embodiments, after synchronously distributing each simulation test subtask and the vehicle attitude corresponding to the target automatic driving system in the simulation test task to the plurality of servers, the method further includes: and setting simulation frequencies of different sensing modules, sending corresponding simulation frequencies to a plurality of servers, and ensuring that the plurality of servers are in a time synchronization state.
In some optional embodiments, the multiple servers may synchronously execute the distributed simulation test subtasks according to the simulation frequency, and perform the simulation test according to the vehicle attitude of the target automatic driving system, including: after each server executes the distributed simulation test subtask, obtaining perception data of at least one perception module; respectively sending perception data obtained by simulating each server according to the simulation frequency of different perception modules to corresponding input ends of an automatic driving system of a test vehicle; and updating the attitude of the test vehicle according to the control signal output by the automatic driving system.
In some optional embodiments, the method is applied to hardware-in-loop simulation or whole vehicle-in-loop simulation, and sends simulation frequencies to a plurality of servers, and comprises: and setting corresponding simulation frequency according to the working frequency of a sensing module of the automatic driving system of the test vehicle, determining a server capable of realizing the corresponding simulation frequency, and sending the corresponding simulation frequency to a plurality of servers.
In the embodiment of the application, when the computational complexity of the simulation test task is too high and the simulation frequency which can be realized by a single server cannot meet the requirements, the test task is disassembled into a plurality of servers to cooperatively operate, the output result is synchronously transmitted to the same automatic driving system, and the true frequency is set as the working frequency of a sensor of the automatic driving system, so that the support of hardware-in-loop simulation and whole-vehicle-in-loop simulation can be realized.
Referring to fig. 2, fig. 2 is a functional block diagram of an automatic driving simulation testing system according to an embodiment of the present disclosure, where the system includes a user interface 100, a simulator cluster 101, a data bus 102, an adapter cluster 103, a system under test 104, a simulation task scheduler 105, and other functional blocks. The environment can be used for application scenarios such as model in-loop simulation, software in-loop simulation, processor in-loop simulation, hardware in-loop simulation, whole vehicle in-loop simulation and the like.
Wherein, the user interface 100 is: and the software program is operated on the user equipment and is provided with a graphical interface and/or a text interface, and a user of the cloud simulation can submit a simulation test task and view and process the simulation test result.
The simulator cluster 101 is composed of a plurality of simulator software instances running on a single or a plurality of servers, and can send data such as simulation results to the user interface 100; each simulator software instance runs on one server and comprises the functions of map analysis and rendering, simulation scene construction, sensor modeling program, simulation result sending and the like; communication latency between emulators is typically on the order of 1 millisecond and communication bandwidth on the order of 1-10 Gbps. The simulator cluster 101 comprises K simulators 1011, wherein K is a positive integer greater than 0, and each simulator 1011 is a certain simulator software example in the simulator cluster 101.
The data bus 102 is used to connect the data interfaces of the different emulators and different signal converters. When the emulator cluster and the signal converter are located in the same server, the data bus 102 is implemented by an interprocess communication mechanism, such as a pipe, a message queue, etc. When the simulator cluster and the signal converter are located in a plurality of servers, the data bus 102 is composed of a high-speed switch, a communication cable and a communication protocol, the communication bandwidth of the data bus is usually in the order of 1-10 Gbps, and the data bus has extremely low communication delay and packet loss rate; common communication protocols include ethernet, infiniBand (IB), and the like.
The adapter cluster 103 is used to adapt a data interface of an assisted driving system or an autonomous driving system. Software and/or hardware devices that simulate the dynamics of an autopilot system have a data interface and are connected to a data bus 102. Adapter cluster 103 is used for software-on-ring, model-on-ring, and processor-on-ring simulations, and is typically comprised of software programs running on one or more servers; the adapter cluster 103 is used for hardware-on-loop and full car-on-loop simulations, and is typically made up of a series of hardware devices and installed in a cabinet.
The adapter cluster 103 includes N signal converters 1031, N being a positive integer greater than 0. Each signal sensor 1031 includes communication and signal conversion functions, and receives data (i.e., simulation result) sent by a server in the simulator cluster via the data bus 102, and converts the data into data required by the system under test 104 in real time, such as data structures in software programs, bus data, electrical signals, optical signals, and the like; these signals appear to the system under test 104 to be indistinguishable from the signals sent by the actual sensors and positioning equipment, thus enabling real-time high fidelity simulation of the sensors.
The number N of signal converters is equal to the total number of all sensors and positioning devices that all autopilot systems have, N may be greater than, equal to, or less than K; the data connection relationships between the K servers and the N signal converters are defined by the simulation task scheduler 105.
The form of the data output by the signal converter to the system under test 104 depends on the type of simulation test task. For example, for model-on-ring, software-on-ring, and processor-on-ring tests, the data is typically in the form of structures, arrays, and/or objects, etc. that conform to some definition; for hardware-on-loop and vehicle-on-loop testing, the data is typically in the form of bus data, electrical and/or optical signals, and the like.
There may be M tested systems 104, i.e. there is a one-to-one correspondence between the autopilot system and the model solver, and each tested system 104 is a tested assistant driving system or autopilot system, which generally consists of a software program and/or a domain controller and its corresponding peripheral, a power supply, and a software program running in the domain controller.
An autopilot system may contain L sensors or positioning devices and other devices to be simulated, which require L signal converters and 1 model solver in an adapter cluster to be adapted to.
The simulation task scheduler 105 is a software program running in the server, which receives a test task submitted by a user through the user interface 100, analyzes the computational complexity of the task, disassembles the simulation test task if necessary, and allocates the disassembled test task to a corresponding server in the simulator cluster.
When the operating frequency of all functional modules in the system is strictly equal to the working frequency of the device, the system can be used for hardware-in-loop simulation and whole vehicle-in-loop simulation; otherwise, the system can only be used for model-in-loop simulation, software-in-loop simulation, and processor-in-loop simulation.
In a specific embodiment, please refer to fig. 3, fig. 3 is a schematic diagram of an autopilot simulation testing system provided in the embodiment of the present application, wherein the autopilot system has 3 sensors, 3 servers, and 3 signal converters. The simulation task scheduler receives a simulation test task, then the simulation test task is disassembled into 3 test tasks corresponding to different sensors, the test tasks of the sensors are respectively distributed to corresponding servers in the simulation task scheduler for simulation, each simulation result is converted into a signal adaptive to a data input interface of the corresponding sensor through a corresponding signal converter, and the signal is input into the automatic driving system.
In another specific embodiment, please refer to fig. 4, fig. 4 is a schematic diagram of an autopilot simulation testing system according to another embodiment of the present application, wherein the autopilot system has 3 sensors, 3 servers, and 3 signal converters. The simulation task scheduler receives the 3 simulation test tasks, then allocates each simulation test task to the corresponding server for simulation, and the simulation result of each server comprises the simulation result of the 3 sensors, so that each server needs to respectively send the simulation results of the 3 sensors to the corresponding signal converter, and each signal sensor is only adapted to one sensor interface of the automatic driving system.
Fig. 5 shows a possible structure of an electronic device provided in an embodiment of the present application. Referring to fig. 5, the electronic device includes: a processor 21, a memory 22, and a communication interface 23, which are interconnected and in communication with each other via a communication bus 24 and/or other form of connection mechanism (not shown).
The Memory 22 includes one or more (Only one is shown in the figure), which may be, but not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an erasable Programmable Read-Only Memory (EPROM), an electrically erasable Programmable Read-Only Memory (EEPROM), and the like. The processor 21, and possibly other components, may access, read and/or write data from the memory 22.
The processor 21 includes one or more (only one shown) which may be an integrated circuit chip having signal processing capabilities. The Processor 21 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Micro Control Unit (MCU), a Network Processor (NP), or other conventional processors; the Processor may also be a dedicated Processor, including a Neural-Network Processing Unit (NPU), a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. Also, when there are a plurality of processors 21, some of them may be general-purpose processors, and the other may be special-purpose processors.
The communication interface 23 includes one or more (only one shown) that can be used to directly or indirectly communicate with other devices for data interaction. The communication interface 23 may include an interface that performs wired and/or wireless communication.
One or more computer readable instructions may be stored in the memory 22, and may be read and executed by the processor 21 to implement the methods provided by the embodiments of the present application.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 5 or may have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof. The electronic device may be a physical device, such as a PC, a laptop, a tablet, a cell phone, a server, an embedded device, etc., or may be a virtual device, such as a virtual machine, a virtualized container, etc. The electronic device is not limited to a single device, and may be a combination of a plurality of devices or a cluster of a large number of devices.
Embodiments of the present application further provide a computer-readable storage medium, where computer-readable instructions are stored, and when the computer-readable instructions are read and executed by a processor of a computer, the computer-readable instructions perform the method provided in the embodiments of the present application. The computer readable storage medium may be embodied as, for example, the memory 22 in the electronic device of fig. 5.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, 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.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. An automated driving simulation test method, the method comprising:
acquiring a simulation test task of a target automatic driving system, and determining the simulation frequency and characteristic information of the simulation test task, wherein the characteristic information is used for representing the complexity of the simulation test task;
dividing the simulation test task into a plurality of simulation test subtasks according to the characteristic information of the simulation test task;
synchronously distributing each simulation test subtask and the vehicle attitude corresponding to the target automatic driving system in the simulation test tasks to a plurality of servers, so that the plurality of servers can synchronously execute the distributed simulation test subtasks according to the simulation frequency, and carry out simulation test according to the vehicle attitude of the target automatic driving system.
2. The method of claim 1, wherein the characteristic information of the simulation test task comprises a simulation complexity of the test vehicle and/or a simulation complexity of the test scenario.
3. The method of claim 2, wherein the obtaining a simulation test task for a target autopilot system and determining characteristic information for the simulation test task comprises:
acquiring the automatic driving system information of the test vehicle in the simulation test task, and determining a vehicle dynamics model solver corresponding to the automatic driving system of the test vehicle;
and determining the simulation complexity of the test vehicle according to the vehicle dynamics model solver.
4. The method of claim 2, wherein the obtaining a simulation test task for a target autopilot system and determining characteristic information for the simulation test task comprises:
acquiring perception module information of a test vehicle in the simulation test task, and determining the type and the number of the perception modules;
and determining the simulation complexity of the test vehicle according to the type and the number of the perception modules.
5. The method according to any one of claims 2-4, wherein the obtaining a simulation test task of the target automatic driving system and the determining the characteristic information of the simulation test task comprise:
acquiring scene elements of a test scene in the simulation test task, and determining action types corresponding to the scene elements;
and determining the simulation complexity of the test scene according to the action type corresponding to the scene element.
6. The method according to claim 4, wherein the dividing the simulation test task into a plurality of simulation test subtasks according to the feature information of the simulation test task comprises:
and dividing simulation test subtasks corresponding to the perception modules of the same type in the simulation test task into a server according to the types of the different perception modules in the simulation test task.
7. The method of claim 6, wherein after synchronously distributing each simulation test subtask and the vehicle pose corresponding to the target autopilot system in the simulation test task to the plurality of servers, further comprising:
and setting simulation frequencies of different sensing modules, sending corresponding simulation frequencies to the plurality of servers, and ensuring that the plurality of servers are in a time synchronization state.
8. The method of claim 7, wherein the plurality of servers can synchronously execute the assigned simulation test subtasks according to the simulation frequency, and perform simulation tests according to the vehicle attitude of the target autopilot system, comprising:
after each server executes the distributed simulation test subtasks, obtaining perception data of at least one perception module;
respectively sending perception data obtained by simulating each server according to the simulation frequency of the different perception modules to corresponding input ends of an automatic driving system of the test vehicle;
and updating the attitude of the test vehicle according to the control signal output by the automatic driving system.
9. The method of any one of claims 7-8, wherein the method is applied to hardware-in-the-loop simulation or whole vehicle-in-the-loop simulation, and the sending the corresponding simulation frequencies to the plurality of servers comprises:
and setting corresponding simulation frequency according to the working frequency of a sensing module of the automatic driving system of the test vehicle, determining a server capable of realizing the corresponding simulation frequency, and sending the corresponding simulation frequency to the plurality of servers.
10. An electronic device, comprising: a processor and a memory, the memory storing machine-readable instructions executable by the processor, the machine-readable instructions, when executed by the processor, performing the method of any of claims 1-9.
11. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 9.
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