CN113126624A - Automatic driving simulation test method, device, electronic equipment and medium - Google Patents
Automatic driving simulation test method, device, electronic equipment and medium Download PDFInfo
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- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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Abstract
The embodiment of the disclosure discloses an automatic driving simulation test method, an automatic driving simulation test device, electronic equipment and a medium. One embodiment of the method comprises: acquiring simulation information, vehicle state information and historical speed information of a target vehicle; carrying out simulation processing on the simulation information to generate target simulation information; generating vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information; and transmitting the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is used for controlling the target vehicle to run according to the vehicle control information. The embodiment improves the accuracy of the test result and the safety of the real vehicle in the driving process.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to an automatic driving simulation test method, an automatic driving simulation test device, electronic equipment and a medium.
Background
The automatic driving simulation test method mainly refers to the application of a computer simulation technology in the field of automobiles. And (3) digitally restoring the application scene of automatic driving by using a mathematical modeling mode, and establishing a system model as close to the real world as possible. Analysis and study were performed by simulation testing. And further achieve the purpose of testing and verifying the automatic driving system and the algorithm. Currently, autonomous vehicles are simulated by employing test simulation software (e.g., preScan software, carsim software, etc.).
However, when the automatic driving simulation test is performed in the above manner, there are often the following technical problems:
firstly, in the process of carrying out the automatic driving simulation test, in order to meet diversified development requirements, multiple types of software are generally required to be combined and configured to complete the test of the automatic driving algorithm in a rich scene, but the cross-software algorithm verification can cause the conditions that the stability of the whole system is poor and the simulation result is inconsistent, so that the accuracy of the test result is low, and the safety of a real vehicle in the driving process is reduced.
Second, when calculating route planning information of autonomous vehicles, vehicle information in traffic flows is often set manually. However, because people have limited acquisition and perception capabilities, the vehicle information is often influenced by subjective consciousness of people when being set in a user-defined mode, so that the setting of the vehicle information in the traffic flow is not accurate enough, real vehicle information cannot be simulated, deviation exists in the measurement and calculation of the path planning information of the automatic driving vehicle, and the safety of the automatic driving vehicle in the driving process is further reduced.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Some embodiments of the present disclosure propose automated driving simulation test methods, apparatuses, electronic devices, and media to address one or more of the technical problems noted in the background section above.
In a first aspect, some embodiments of the present disclosure provide an automated driving simulation test method, the method comprising: acquiring simulation information, vehicle state information and historical speed information of a target vehicle, wherein the simulation information of the target vehicle comprises: position information, wheel state information, speed information, and obstacle information, the historical speed information including: historical vehicle longitudinal speed values, the vehicle state information comprising: body weight and travel grade; carrying out simulation processing on the simulation information to generate target simulation information; generating vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information; and sending the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is used for controlling the target vehicle to run according to the vehicle control information.
In a second aspect, some embodiments of the present disclosure provide an automated driving simulation test apparatus, the apparatus comprising: an acquisition unit configured to acquire simulation information of a target vehicle, the vehicle state information, and historical speed information, wherein the simulation information of the target vehicle includes: position information, wheel state information, speed information, and obstacle information, the historical speed information including: historical vehicle longitudinal speed values, the vehicle state information comprising: body weight and travel grade; a simulation processing unit configured to perform simulation processing on the simulation information to generate target simulation information; a generation unit configured to generate vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information; a transmission unit configured to transmit the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is configured to control the target vehicle to travel according to the vehicle control information.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: by the automatic driving simulation test method of some embodiments of the disclosure, the accuracy of the test result and the safety of the real vehicle in the driving process are improved. Specifically, the reasons for the low accuracy of the test results are: in the process of carrying out the automatic driving simulation test, in order to meet diversified development requirements, multiple types of software are generally required to be combined and configured to complete the test of the automatic driving algorithm in a rich scene, but the cross-software algorithm verification can cause the conditions that the stability of the whole system is poor and the simulation result is inconsistent, so that the accuracy of the test result is low, and the safety of a real vehicle in the driving process is further reduced. Based on this, the automatic driving simulation test method of some embodiments of the present disclosure designs a set of systems to satisfy the verification of different stages of the algorithm, thereby solving the problem of inconsistent simulation results. First, simulation information, vehicle state information, and historical speed information of a target vehicle are acquired. The acquired information provides data support for subsequent simulation processing. Next, the simulation information is subjected to simulation processing to generate target simulation information. The target vehicle is subjected to flexible scene information configuration and simulation processing to simulate the real scene of the vehicle, so that the intelligent and automatic generation of environment and traffic flow is realized, the utilization rate of road acquisition data is improved, and the algorithm iteration speed is further improved. Then, vehicle control information is generated based on the target simulation information, the vehicle state information, and the historical speed information. The generated vehicle control information then provides data support for subsequently controlling vehicle travel. And finally, transmitting the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is used for controlling the target vehicle to run according to the vehicle control information. The vehicle control information is fed back to the test vehicle in the simulation tool, and the test vehicle makes a corresponding response after receiving the control information, so that a hardware closed loop is formed. Optionally, the simulation test information is sent to a target terminal of the target vehicle as feedback state information. The trained test result is returned to the local, and the algorithm is finely debugged according to the test result, so that the efficiency of developing and testing the automatic driving algorithm can be effectively improved. The verification in different stages is met through the fusion of a plurality of algorithms such as perception, positioning, planning and control, the use requirements of the model in the ring, the software in the ring, the hardware in the ring and the vehicle in the ring are met, the condition that simulation results are inconsistent due to cross-software algorithm verification is avoided, and the accuracy of test results and the safety of a real vehicle in the driving process are improved.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic illustration of one application scenario of an automated driving simulation testing method of some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an automated driving simulation testing method according to the present disclosure;
FIG. 3 is a schematic structural diagram of some embodiments of an automated driving simulation test apparatus according to the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 is a schematic diagram of one application scenario of an automated driving simulation testing method of some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may obtain simulation information 102, vehicle state information 103, and historical speed information 104 of a target vehicle, where the simulation information 102 of the target vehicle includes: position information, wheel state information, speed information, and obstacle information, the historical speed information 104 includes: the vehicle state information 103 includes, in the history of the vehicle longitudinal speed value: body weight and travel grade. Next, the computing device 101 may perform a simulation process on the simulation information 102 described above to generate target simulation information 105. Then, the computing device 101 may generate the vehicle control information 106 based on the above-described target simulation information 105, the above-described vehicle state information 103, and the above-described historical speed information 104. Finally, the computing device 101 may transmit the vehicle control information 106 to a control device of the target vehicle for controlling the target vehicle to travel according to the vehicle control information to generate simulation test information 107.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of an automated driving simulation testing method according to the present disclosure is shown. The automatic driving simulation test method comprises the following steps:
in step 201, simulation information, vehicle state information, and historical speed information of a target vehicle are obtained.
In some embodiments, the execution subject of the automated driving simulation test method (e.g., the computing device 101 shown in fig. 1) may acquire the simulation information, the vehicle state information, and the historical speed information of the target vehicle by wired connection or wireless connection. The simulation information of the target vehicle may include, but is not limited to, at least one of the following: position information, wheel state information, speed information, and obstacle information. The historical speed information may include, but is not limited to, at least one of: historical vehicle longitudinal speed values, the vehicle status information may include, but is not limited to, at least one of: body weight and travel grade. The position information may be latitude and longitude information and a relative altitude value of the target vehicle acquired from a global positioning system. The wheel state information may include: the radius value of the front wheel and the radius value of the rear wheel of the target vehicle. The speed information may include: the front wheel angular velocity value and the rear wheel angular velocity value of the above-described target vehicle. The obstacle information may be a real-time traffic image captured by a smart camera installed in the target vehicle. The historical vehicle longitudinal speed value may be a longitudinal speed value of the target vehicle. The body weight may be a total vehicle trim mass of the target vehicle. The running gradient may be a longitudinal gradient of the road on which the target vehicle is running.
As an example, the simulation information of the target vehicle may be: "[ 39.92646, 116.64055], 8400 meters, 3.15 decimeters, 1.1 radians per second, 1.2 radians per second, image 1". The vehicle state information may be: "1600 kg, 2.8624 degrees". The historical speed information may be "50 kilometers per hour".
In some embodiments, the executing entity (e.g., the computing device 101 shown in fig. 1) may perform a fusion process on the simulation information using a multi-sensor information fusion algorithm to generate the target simulation information. The multi-sensor information fusion algorithm may include: weighted average algorithm, least squares, kalman filter algorithm, etc. The multi-sensor data fusion algorithm can be used for eliminating redundancy and contradiction possibly existing among multi-sensor information, complementation is carried out, and the uncertainty of the multi-sensor information is reduced, so that the rapidness and the correctness of system decision, planning and reaction are improved.
In some optional implementations of some embodiments, the executing entity performs simulation processing on the simulation information to generate target simulation information, where the target simulation information may include, but is not limited to, at least one of the following: the target positioning information, the vehicle stress information and the target obstacle information may include the steps of:
firstly, simulating the position information included in the simulation information to generate target positioning information included in the target simulation information.
The execution subject can firstly acquire the initial pose of the target vehicle by using the perception sensor. The above-mentioned perception sensors may include, but are not limited to, at least one of: odometers, gyroscopes, etc. Then, the execution subject may generate the target positioning information included in the target simulation information by using a triangulation method based on the initial pose and the position information included in the simulation information.
And secondly, generating vehicle stress information included in the target simulation information based on the wheel state information, the speed information and the history included in the simulation information.
Wherein, the vehicle stress information may include, but is not limited to, at least one of the following: front wheel tire longitudinal force and rear wheel tire longitudinal force.
As an example, the executing body may generate the front-wheel tire longitudinal force and the rear-wheel tire longitudinal force included in the vehicle stress information included in the target simulation information, based on the wheel state information, the speed information, and the historical speed information included in the simulation information, in response to the acceleration value of the target vehicle being positive, by the following formulas:
wherein, F1And indicating the longitudinal force of the front wheel tire included in the vehicle stress information included in the target simulation information. F2And indicating the longitudinal force of the rear wheel tire included in the vehicle stress information included in the target simulation information. a represents a historical vehicle longitudinal speed value included in the above historical speed information. r is1And a step of representing a radius value of the front wheel included in the wheel state information included in the simulation information. Omega1Indicating front wheels included in speed information included in the above-described simulation informationThe angular velocity value. Omega2Indicating the rear wheel angular velocity value included in the velocity information included in the above-described simulation information.
As still another example, the executing body may generate the front-wheel tire longitudinal force and the rear-wheel tire longitudinal force included in the vehicle stress information included in the target simulation information, based on the wheel state information, the speed information, and the historical speed information included in the simulation information, in response to the acceleration value of the target vehicle being negative, by:
wherein, F1And indicating the longitudinal force of the front wheel tire included in the vehicle stress information included in the target simulation information. F2And indicating the longitudinal force of the rear wheel tire included in the vehicle stress information included in the target simulation information. a represents a historical vehicle longitudinal speed value included in the above historical speed information. r is1And a step of representing a radius value of the front wheel included in the wheel state information included in the simulation information. r is2And a step of representing a rear wheel radius value included in the wheel state information included in the simulation information. Omega1Indicating the front wheel angular velocity value included in the velocity information included in the above-described simulation information. Omega2Indicating the rear wheel angular velocity value included in the velocity information included in the above-described simulation information.
And thirdly, generating target obstacle information included in the target simulation information based on the obstacle information included in the simulation information.
The executing body may generate target obstacle information included in the target simulation information based on the obstacle information included in the simulation information, and may perform the following steps:
a first substep of performing image classification on the obstacle information included in the simulation information to generate obstacle classification information. The executing body may perform image classification on the obstacle information included in the simulation information using a minimum distance classification algorithm to generate obstacle classification information.
And a second substep of performing object positioning processing on the obstacle information included in the simulation information to generate obstacle position information. The executing body may perform object positioning processing on the obstacle information included in the simulation information by using an artificial neural network algorithm to generate obstacle position information.
A third substep of combining the obstacle classification information and the obstacle position information to generate the target obstacle information.
As an example, the above-described obstacle classification information may be [ vehicle, pedestrian, road sign ]. The above-mentioned obstacle position information may be [ [39.82646, 115.64055], [39.92646, 116.64055] ]. The above target obstacle information may be [ vehicle, pedestrian, road sign, [ [39.82646, 115.64055], [39.92646, 116.64055] ].
In step 203, vehicle control information is generated based on the target simulation information, the vehicle state information, and the historical speed information.
In some embodiments, the executing agent (e.g., computing device 101 shown in fig. 1) may generate vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information using an autonomous driving longitudinal control algorithm. The automatic driving longitudinal control algorithm may be a PID (Proportional Integral Derivative) control algorithm. The above-described autopilot longitudinal Control algorithm may be an MPC (Model Predictive Control) algorithm.
In some optional implementations of some embodiments, the execution subject may generate vehicle control information based on target simulation information, vehicle state information, and historical speed information, wherein the path planning information includes: longitudinal motion velocity values. The executing agent may generate the longitudinal movement speed value included in the path planning information by using the following formula based on the vehicle stress information included in the target simulation information, the historical vehicle longitudinal speed value included in the historical speed information, and the vehicle body weight and the traveling gradient included in the vehicle state information:
wherein, F1And indicating the longitudinal force of the front wheel tire included in the vehicle stress information included in the target simulation information. F2And indicating the longitudinal force of the rear wheel tire included in the vehicle stress information included in the target simulation information. F3Indicating longitudinal air resistance. ρ represents the air density under standard conditions. The value is 1.293kg/m3And C represents a preset air resistance coefficient. The value range is [0.4,0.6 ]]. f represents a preset rolling resistance coefficient. The value range is [0.01 ]. 0.05]. M represents the vehicle body weight included in the vehicle state information. R represents front and rear wheel rolling resistance. g represents the gravitational acceleration. The value is 9.8m/s2. θ represents a traveling gradient included in the vehicle state information. a represents a historical vehicle longitudinal speed value included in the above historical speed information. v represents a longitudinal motion velocity value included in the path planning information.
The formula and the related content in step 203 are used as an invention point of the present disclosure, and the technical problem mentioned in the background art is solved, namely "the safety during the automatic driving process is low due to deviation existing in the measurement and calculation of the path planning information of the automatic driving vehicle". Factors that cause lower safety during automated driving tend to be as follows: when the route planning information of the autonomous vehicle is measured and calculated, the vehicle information in the traffic flow is often artificially set. However, because people have limited acquisition and perception capabilities, the vehicle information is often influenced by subjective consciousness of people when being set in a user-defined mode, so that the setting of the vehicle information in the traffic flow is not accurate enough, real vehicle information cannot be simulated, deviation exists in the measurement and calculation of the path planning information of the automatic driving vehicle, and the safety of the automatic driving vehicle in the driving process is further reduced. If the above-mentioned factors are solved, the effect of safety in the automatic driving process can be improved. To achieve this, the present disclosure first generates target simulation information by simulating an in-vehicle sensor, wherein the target simulation information is used to simulate vehicle information in a real scene. The vehicle information may include, but is not limited to, at least one of: vehicle position information, vehicle speed information. And secondly, based on the target simulation information, the path planning information generated by performing path planning processing on the target vehicle can avoid the problem that the actual traffic flow scene is not consistent when the vehicle information is set manually, so that the measurement and calculation precision of the path planning information of the automatic driving vehicle is improved, and the safety of the automatic driving vehicle in the driving process is improved.
And a second step of generating the vehicle control information based on the path planning information.
The execution main body may first generate a control signal based on the longitudinal motion velocity value included in the path planning information. The control signal may include, but is not limited to, at least one of: throttle opening degree, brake opening degree, steering wheel angle and front wheel rotation angle. Next, the execution body may control the target vehicle based on the control signal to generate vehicle control information. The vehicle control information may include, but is not limited to: target vehicle position information.
In some embodiments, the execution subject (e.g., the computing device 101 shown in fig. 1) may transmit vehicle control information to a control device of the target vehicle for controlling the target vehicle to travel according to the vehicle control information to generate the simulation test information.
Optionally, the simulation test information is sent to the target terminal of the target vehicle as feedback state information.
The above embodiments of the present disclosure have the following advantages: by the automatic driving simulation test method of some embodiments of the disclosure, the accuracy of the test result and the safety of the real vehicle in the driving process are improved. Specifically, the reasons for the low accuracy of the test results are: in the process of carrying out the automatic driving simulation test, in order to meet diversified development requirements, multiple types of software are generally required to be combined and configured to complete the test of the automatic driving algorithm in a rich scene, but the cross-software algorithm verification can cause the conditions that the stability of the whole system is poor and the simulation result is inconsistent, so that the accuracy of the test result is low, and the safety of a real vehicle in the driving process is further reduced. Based on this, the automatic driving simulation test method of some embodiments of the present disclosure designs a set of systems to satisfy the verification of different stages of the algorithm, thereby solving the problem of inconsistent simulation results. First, simulation information, vehicle state information, and historical speed information of a target vehicle are acquired. The acquired information provides data support for subsequent simulation processing. Next, the simulation information is subjected to simulation processing to generate target simulation information. The target vehicle is subjected to flexible scene information configuration and simulation processing to simulate the real scene of the vehicle, so that the intelligent and automatic generation of environment and traffic flow is realized, the utilization rate of road acquisition data is improved, and the algorithm iteration speed is further improved. Then, vehicle control information is generated based on the target simulation information, the vehicle state information, and the historical speed information. The generated vehicle control information then provides data support for subsequently controlling vehicle travel. And finally, transmitting the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is used for controlling the target vehicle to run according to the vehicle control information. The vehicle control information is fed back to the test vehicle in the simulation tool, and the test vehicle makes a corresponding response after receiving the control information, so that a hardware closed loop is formed. Optionally, the simulation test information is sent to a target terminal of the target vehicle as feedback state information. The trained test result is returned to the local, and the algorithm is finely debugged according to the test result, so that the efficiency of developing and testing the automatic driving algorithm can be effectively improved. The verification in different stages is met through the fusion of a plurality of algorithms such as perception, positioning, planning and control, the use requirements of the model in the ring, the software in the ring, the hardware in the ring and the vehicle in the ring are met, the condition that simulation results are inconsistent due to cross-software algorithm verification is avoided, and the accuracy of test results and the safety of a real vehicle in the driving process are improved.
With further reference to fig. 3, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of an automated driving simulation test apparatus, which correspond to those illustrated in fig. 2, and which may be particularly applicable in various electronic devices.
As shown in fig. 3, the automated driving simulation test apparatus 300 of some embodiments includes: an acquisition unit 301, a simulation processing unit 302, a generation unit 303, and a transmission unit 304, wherein the acquisition unit 301 is configured to acquire simulation information of a target vehicle, the vehicle state information, and historical speed information, wherein the simulation information of the target vehicle includes: position information, wheel state information, speed information, and obstacle information, the historical speed information including: historical vehicle longitudinal speed values, the vehicle state information comprising: body weight and travel grade; a simulation processing unit 302 configured to perform simulation processing on the simulation information to generate target simulation information; a generating unit 303 configured to generate vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information; a transmitting unit 304 configured to transmit the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is configured to control the target vehicle to travel according to the vehicle control information.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Referring now to FIG. 4, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)400 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 404 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing apparatus 401, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring simulation information, vehicle state information and historical speed information of a target vehicle, wherein the simulation information of the target vehicle comprises: position information, wheel state information, speed information, and obstacle information, the historical speed information including: historical vehicle longitudinal speed values, the vehicle state information comprising: body weight and travel grade; carrying out simulation processing on the simulation information to generate target simulation information; generating vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information; and sending the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is used for controlling the target vehicle to run according to the vehicle control information.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a simulation processing unit, a generation unit, and a transmission unit. Here, the names of these units do not constitute a limitation of the unit itself in some cases, and for example, the acquisition unit may also be described as a "unit that acquires simulation information, vehicle state information, and historical speed information of the target vehicle".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. An automated driving simulation test method, comprising:
acquiring simulation information, vehicle state information and historical speed information of a target vehicle, wherein the simulation information of the target vehicle comprises: position information, wheel state information, speed information, and obstacle information, the historical speed information including: historical vehicle longitudinal speed values, the vehicle state information comprising: body weight and travel grade;
carrying out simulation processing on the simulation information to generate target simulation information;
generating vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information;
and sending the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is used for controlling the target vehicle to run according to the vehicle control information.
2. The method of claim 1, wherein the method further comprises:
and sending the simulation test information serving as feedback state information to a target terminal of the target vehicle.
3. The method of claim 2, wherein the target simulation information comprises: target positioning information, vehicle stress information and target obstacle information; and
the simulating the simulation information to generate target simulation information includes:
performing simulation processing on the position information included in the simulation information to generate target positioning information included in the target simulation information;
generating vehicle stress information included in the target simulation information based on wheel state information, speed information and the historical speed information included in the simulation information, wherein the vehicle stress information includes: front wheel tire longitudinal force, rear wheel tire longitudinal force;
and generating target obstacle information included in the target simulation information based on the obstacle information included in the simulation information.
4. The method of claim 3, wherein the generating target obstacle information included in the target simulation information based on obstacle information included in the simulation information comprises:
performing image classification on obstacle information included in the simulation information to generate obstacle classification information;
carrying out object positioning processing on obstacle information included in the simulation information to generate obstacle position information;
combining the obstacle classification information and the obstacle location information to generate the target obstacle information.
5. The method of claim 4, wherein the generating vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information comprises:
performing path planning processing on the target vehicle based on the target simulation information, the vehicle state information and the historical speed information to generate path planning information;
and generating the vehicle control information based on the path planning information.
6. The method of claim 5, wherein the path planning information comprises: a longitudinal motion velocity value; and
the performing path planning processing on the target vehicle based on the target simulation information, the vehicle state information, and the historical speed information to generate path planning information includes:
and generating a longitudinal motion speed value included in the path planning information based on vehicle stress information included in the target simulation information, a longitudinal speed value of the vehicle included in the historical speed information, and the vehicle body weight and the running gradient included in the vehicle state information.
7. The method of claim 6, wherein the generating the longitudinal motion speed value included in the path planning information based on the vehicle stress information included in the target simulation information, the historical vehicle longitudinal speed value included in the historical speed information, and the body weight and the traveling gradient included in the vehicle state information comprises:
based on the vehicle stress information included in the target simulation information, the historical vehicle longitudinal speed value included in the historical speed information, and the vehicle body weight and the running gradient included in the vehicle state information, generating the longitudinal movement speed value included in the path planning information by the following formula:
wherein, F1Indicating the longitudinal force of the front tire included in the vehicle stress information included in the target simulation information, F2Indicating the longitudinal force of the rear wheel tire included in the vehicle stress information included in the target simulation information, F3Represents the longitudinal air resistance, and rho represents the air density under the standard condition, and the value is 1.293kg/m3C represents a preset air resistance coefficient, and the value range is [0.4,0.6 ]]F represents a preset rolling resistance coefficient, and the numeric area is [0.01, 0.05 ]]M represents the vehicle body weight included in the vehicle state information, R represents the rolling resistance of front and rear wheels, g represents the gravity acceleration, and the value is 9.8M/s2Theta denotes a traveling gradient included in the vehicle state information, a tableAnd v represents a longitudinal motion speed value included in the path planning information.
8. An automated driving simulation test apparatus comprising:
an acquisition unit configured to acquire simulation information, vehicle state information, and historical speed information of a target vehicle, wherein the simulation information of the target vehicle includes: position information, wheel state information, speed information, and obstacle information, the historical speed information including: historical vehicle longitudinal speed values, the vehicle state information comprising: body weight and travel grade;
a simulation processing unit configured to perform simulation processing on the simulation information to generate target simulation information;
a generation unit configured to generate vehicle control information based on the target simulation information, the vehicle state information, and the historical speed information;
a transmission unit configured to transmit the vehicle control information to a control device of the target vehicle to generate simulation test information, wherein the control device is configured to control the target vehicle to travel according to the vehicle control information.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
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