CN117434855A - Automatic driving simulation method and system - Google Patents
Automatic driving simulation method and system Download PDFInfo
- Publication number
- CN117434855A CN117434855A CN202311488933.5A CN202311488933A CN117434855A CN 117434855 A CN117434855 A CN 117434855A CN 202311488933 A CN202311488933 A CN 202311488933A CN 117434855 A CN117434855 A CN 117434855A
- Authority
- CN
- China
- Prior art keywords
- simulation
- vehicle
- road
- data
- virtual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004088 simulation Methods 0.000 title claims abstract description 300
- 238000000034 method Methods 0.000 title claims abstract description 95
- 238000012360 testing method Methods 0.000 claims abstract description 48
- 230000008569 process Effects 0.000 claims abstract description 37
- 238000009877 rendering Methods 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims description 30
- 238000010276 construction Methods 0.000 claims description 25
- 230000007613 environmental effect Effects 0.000 claims description 20
- 230000003993 interaction Effects 0.000 claims description 20
- 238000003860 storage Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004422 calculation algorithm Methods 0.000 claims description 12
- 230000004044 response Effects 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 5
- 230000006870 function Effects 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 5
- 238000005457 optimization Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000002452 interceptive effect Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000011981 development test Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses an automatic driving simulation method and system. Comprising the following steps: rendering virtual environment data and a vehicle dynamics model, wherein the virtual environment data comprises a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road; automatic driving simulation is carried out based on the virtual simulation road, and in the simulation process: and acquiring first running data of the vehicle simulation equipment and second running data of the vehicle dynamics model, wherein the first running data and the second running data form current running data, determining predicted running data of the vehicle on the virtual simulation road based on the current running data, determining vehicle control information based on the predicted running data, controlling the vehicle simulation equipment to perform automatic driving simulation based on the vehicle control information, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information. The scheme realizes the joint simulation test, improves the accuracy of the automatic driving simulation test, and reduces the test cost.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to an automatic driving simulation method and system.
Background
The automatic driving technology is a technology for realizing automatic navigation, automatic control and intelligent decision of a vehicle through a computer system and sensor equipment, and the occurrence and development of the automatic driving technology bring revolutionary changes to the field of transportation.
The safety requirement in the field of automatic driving of automobiles is very high, but the test cost is too high due to the fact that a real automobile test scheme is adopted, and the problems of inaccurate test and low efficiency exist due to the fact that an automatic driving model of an automobile is built for testing.
Disclosure of Invention
The invention provides an automatic driving simulation method and system, which are used for solving the problems of over-high test cost, inaccurate automatic driving test and low efficiency in the prior art.
According to an aspect of the present invention, there is provided an automatic driving simulation method including:
rendering virtual environment data and a vehicle dynamics model, wherein the virtual environment data comprises a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road;
automatic driving simulation is carried out based on the virtual simulation road, and in the simulation process: and acquiring first running data of the vehicle simulation equipment and second running data of the vehicle dynamics model, wherein the first running data and the second running data form current running data, determining predicted running data of the vehicle on the virtual simulation road based on the current running data, determining vehicle control information based on the predicted running data, controlling the vehicle simulation equipment to perform automatic driving simulation based on the vehicle control information, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information.
Optionally, the method for creating the virtual simulation road includes at least one of the following:
acquiring a simulation demand configuration file, wherein the simulation demand configuration file comprises at least one road configuration information; constructing a simulated road section based on the road configuration information, and forming a virtual simulated road based on at least one simulated road section;
detecting road construction operation in the simulation interaction page, wherein the road construction operation comprises road configuration information; constructing a simulated road section based on the road configuration information, and forming a virtual simulated road based on at least one simulated road section;
detecting road construction operation in the simulation interaction page, wherein the road construction operation comprises road configuration information; matching a target simulation road section in the constructed simulation road sections based on the road configuration information, and forming a virtual simulation road based on the target simulation road section;
in the simulation interaction page, at least one simulation road section is called from a pre-created road library in response to a road calling operation, and a virtual simulation road is formed based on the at least one simulation road section.
Optionally, the method further comprises:
forming a simulation path based on a starting point and an end point in the virtual simulation road, and sequentially setting a reference point and index information of the reference point for the simulation path;
Correspondingly, the current time driving data comprise vehicle position information at the current time, vehicle speed information at the current time, vehicle yaw angle data at the current time, vehicle steering wheel corner data at the current time, index information of the nearest reference point of the vehicle position at the last time and a position set of the reference point in the simulation path;
the predicted travel data includes reference path information corresponding to the predicted time step, vehicle speed information at the next time, vehicle yaw angle data at the next time, vehicle steering wheel angle data at the next time, and index information of the nearest reference point at the current time.
Optionally, the vehicle speed information in the simulation process is preset average speed data, and the vehicle yaw angle data comprises a preset yaw angle corresponding to each reference point; the steering wheel angle data of the vehicle comprises preset steering wheel angle data corresponding to each reference point;
the determination mode of the reference path information corresponding to the predicted time step comprises the following steps: and determining reference index information corresponding to a preset time step after index information of the nearest reference point of the vehicle position at the last moment, and taking the reference point position information corresponding to the reference index information as reference path information corresponding to the predicted time step.
Optionally, determining the vehicle control information based on the predicted driving data includes:
and performing simulation processing on the predicted running data based on the MPC algorithm to obtain vehicle control information, wherein the vehicle control information comprises a vehicle front wheel deflection angle at the next moment, vehicle position information at the next moment, vehicle speed at the next moment, a vehicle yaw angle at the next moment, a steering wheel corner at the next moment and vehicle acceleration at the next moment.
Optionally, the method further comprises:
and acquiring environmental data of the vehicle dynamics model in the running process of the vehicle dynamics model on the virtual simulation road through a simulation sensor configured on the vehicle dynamics model, and detecting abnormal running of the vehicle dynamics model based on the environmental data.
Optionally, the method further comprises:
acquiring driving data of a vehicle dynamics model and driving data of vehicle simulation equipment in a simulation process;
in the case where there is a difference between the running data of the vehicle dynamics model and the running data of the vehicle simulation apparatus, difference comparison data is generated based on the running data of the vehicle dynamics model and the running data of the vehicle simulation apparatus, the difference comparison data being used to optimize the vehicle dynamics model.
According to another aspect of the present invention, there is provided an automatic driving simulation system including: the system comprises a simulation test system, a traffic scene server and vehicle simulation equipment; the simulation test system comprises a road construction module, an automatic driving processing module and a vehicle dynamics model;
the traffic scene server pre-stores virtual environment data;
the road construction module is used for constructing a virtual simulation road in the virtual environment data;
the automatic driving processing module is used for rendering virtual environment data and a vehicle dynamics model on the virtual simulation road, and carrying out automatic driving simulation based on the virtual simulation road, wherein in the simulation process: acquiring first running data of a vehicle simulation device and second running data of a vehicle dynamics model, wherein the first running data and the second running data form current running data, determining predicted running data of a vehicle on a virtual simulation road based on the current running data, determining vehicle control information based on the predicted running data, transmitting the vehicle control information to the vehicle simulation device, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information;
A vehicle simulation apparatus is provided with a travel response means for executing vehicle control information to perform an automatic driving simulation.
Optionally, the simulation test system further comprises a sensor module, wherein the sensor module is used for acquiring environmental data of the vehicle dynamics model in the running process on the virtual simulation road and sending the environmental data to the automatic driving processing module;
the automatic driving processing module detects abnormal driving of the vehicle dynamics model based on the environmental data.
Optionally, the vehicle simulation device is configured with at least degrees of freedom components for pitch, yaw and yaw.
According to another aspect of the present invention, there is provided a simulation test system including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the autopilot simulation method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute an autopilot simulation method of any one of the embodiments of the present invention.
According to the technical scheme, virtual environment data and a vehicle dynamics model are rendered, wherein the virtual environment data comprises a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road; automatic driving simulation is carried out based on the virtual simulation road, and in the simulation process: the method comprises the steps of obtaining first running data of the vehicle simulation equipment and second running data of the vehicle dynamics model, wherein the first running data and the second running data form current running data, determining predicted running data of a vehicle on a virtual simulation road based on the current running data, determining vehicle control information based on the predicted running data, controlling the vehicle simulation equipment to conduct automatic driving simulation based on the vehicle control information, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information, so that the test environment of the virtual simulation road is built, the automatic driving simulation is conducted by combining the vehicle simulation equipment, joint simulation test is achieved, automatic driving simulation test accuracy is improved, development test efficiency is improved, and test cost is reduced.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an autopilot simulation method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a test structure of a driving simulation device according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an autopilot simulation system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a simulation test system implementing an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of an autopilot simulation method according to an embodiment of the present invention, where the method may be performed by an autopilot simulation system, and the autopilot simulation system may be implemented in hardware and/or software, and the autopilot simulation system may be configured in electronic devices such as a vehicle controller, a computer, a simulation device, and the like. As shown in fig. 1, the method includes:
And S110, rendering virtual environment data and a vehicle dynamics model, wherein the virtual environment data comprises a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road.
The virtual environment data may be understood as, in particular, data describing the characteristics of the environment, in particular, data describing the characteristics of the vehicle driving environment, and may include virtual simulation roads constructed in advance. The virtual simulation road constructed in advance can be constructed by a preset creation method. The vehicle dynamics model is used for simulating the response of the vehicle to the control of an automatic driving algorithm, particularly to acceleration, braking and steering, and the whole vehicle dynamics module generally refers to a real target vehicle and consists of a driver model, an environment model, a vehicle body model, a chassis model, a suspension model, a tire model, a steering model, a power model, a braking model and a control model. In this embodiment, the autopilot algorithm module core is a model predictive control (Model Predictive Control) module built based on the mworks. Sysplorer software platform and the Modelica language.
Specifically, the virtual environment data and the vehicle dynamics model are rendered through a rendering technology, and a corresponding visual virtual simulation road and a visual vehicle model are obtained. By way of example, the virtual environment data and the vehicle dynamics model can be rendered through the high-performance computing engine, corresponding three-dimensional images are generated in real time, three-dimensional linkage is supported, and the rendering speed is not lower than 60 frames/second.
In some embodiments, the method for creating the virtual simulation road may be by obtaining a simulation demand configuration file, where the simulation demand configuration file includes at least one road configuration information; a simulated road segment is constructed based on the road configuration information, and a virtual simulated road is formed based on at least one simulated road segment.
The simulation demand file may be specifically understood as a file describing relevant parameters required for constructing a virtual simulation road, where the simulation demand configuration file includes at least one road configuration information. The road configuration information includes a road type and a road type parameter, and in an exemplary case where the road type is a straight road, the road type parameter may be a length, in a case where the road type is an uphill road, the road type parameter may be a gradient, in a case where the road type is a bumpy road, the road type parameter may be a bumpy coefficient, the road type may also be a wet road section, a curve, etc., and the specific road configuration information is set according to actual road simulation requirements.
Specifically, a corresponding simulation demand configuration file is determined according to actual simulation demands, the simulation demand configuration file can be stored in a server or local storage equipment, when the corresponding simulation demands are carried out, the corresponding simulation demand configuration file is read from the server or the local storage equipment, or the simulation demand configuration file is imported from external equipment, at least one road configuration information in the simulation demand configuration file is further read, a corresponding simulation road section is constructed according to the read road configuration information, if only one section of simulation road section is provided, the simulation road section is directly used as a virtual simulation road section, and if a plurality of simulation road sections are constructed, the obtained simulation road sections are spliced to form the virtual simulation road section.
In some embodiments, the method for creating a virtual simulation road may detect a road construction operation in a simulation interaction page, where the road construction operation includes road configuration information; a simulated road segment is constructed based on the road configuration information, and a virtual simulated road is formed based on at least one simulated road segment.
Specifically, a control capable of adding, deleting and checking road configuration information is arranged in the simulation interaction page, and the system jumps to the simulation interaction page under the condition that road construction operation is detected. In the simulation interaction page, a system user can edit corresponding road configuration information in the current simulation interaction page, and under the condition that the road configuration information is received, the system constructs a corresponding simulation road section according to the road configuration information, and forms a virtual simulation road based on at least one simulation road section. It should be noted that the constructed virtual simulation road segments can be stored, so that the constructed virtual simulation road segments can be recycled, the virtual simulation road segments do not need to be reconstructed every time, and the construction efficiency of the virtual simulation road segments is improved.
In some embodiments, the method for creating the virtual simulation road may be to detect a road construction operation in the simulation interaction page, where the road construction operation includes road configuration information; and matching the target simulation road section in the constructed simulation road sections based on the road configuration information, and forming a virtual simulation road based on the target simulation road section.
Specifically, in the simulation interaction page, under the condition that the road construction operation is detected, a target simulation road section can be matched from constructed simulation road sections according to road configuration information edited in the simulation interaction page, if the road configuration information corresponding to the constructed simulation road section is successfully matched with the road configuration information edited in the simulation interaction page, a virtual simulation road is further formed based on the target simulation road section, the construction speed of the virtual simulation road is improved, and the construction speed of the virtual simulation road is further improved.
In some embodiments, the method for creating the virtual simulation road may be to call at least one simulation road section from a road library created in advance in response to a road call operation in the simulation interactive page, and form the virtual simulation road based on the at least one simulation road section.
The pre-created road library is specifically understood to be used for storing a simulated road section which is built according to the preset road configuration information, in the process, the preset road configuration information can be firstly researched through requirements or analyzed on the running environment of the vehicle to obtain a plurality of road configuration information, the corresponding simulated road section is built and stored, the running road scene of the vehicle can be met as much as possible, and the fact that the pre-created road library can be updated according to actual requirements is required, and the simulated road section which does not exist in the road library can be added into the road library in the test process, so that the simulated road section in the road library is continuously perfected. The road calling operation may be by setting an operation button on the interactive page, selecting a label or an image in a road display list in the simulated interactive page, or directly inputting a road type name to search, which is not limited herein.
Specifically, in the simulation interaction page, when the road calling operation is detected to be started, the simulation interaction page calls the simulation road section in the pre-created road base in response to the road calling operation, and the required simulation road section can be matched according to the road calling operation, or at least one simulation road section in the pre-created road base is directly called, and a virtual simulation road is formed through the at least one simulation road section.
It should be noted that the method for creating the virtual simulation road may include one or more of the above-described methods for creating the virtual simulation road. The road configuration information is edited in the simulation interaction page, a target simulation road section is matched in the constructed simulation road sections according to the configuration information, if the matching is unsuccessful, a simulation road section is constructed based on the road configuration information, and a virtual simulation road is formed based on at least one simulation road section. In the case that two or more than two simulated road sections are constructed, each simulated road section needs to be spliced to obtain a new simulated road section, the splicing method can splice the obtained simulated road sections sequentially, and the spliced simulated road sections are subjected to rationality verification, wherein the rationality verification can be set according to the requirements of the simulated roads, and an included angle formed by two simulated road sections at the splicing point of the two simulated road sections is verified to be not smaller than a preset angle value, if the included angle is not satisfied, the current two simulated road sections are not reasonable, the splicing mode needs to be readjusted, and whether the condition that traffic light devices are densely distributed on the spliced simulated road exists can be verified, if the traffic light devices exist, the unreasonable phenomenon of the splicing of the simulated road sections is indicated, and the corresponding rationality verification mode can be set according to the actual requirements, so that the situation is not limited.
In this embodiment, the virtual environment data includes a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road by rendering the virtual environment data and the vehicle dynamics model, so that the user can more intuitively observe the result of the autopilot simulation.
S120, performing automatic driving simulation based on the virtual simulation road, wherein in the simulation process: and acquiring first running data of the vehicle simulation equipment and second running data of the vehicle dynamics model, wherein the first running data and the second running data form current running data, determining predicted running data of the vehicle on the virtual simulation road based on the current running data, determining vehicle control information based on the predicted running data, controlling the vehicle simulation equipment to perform automatic driving simulation based on the vehicle control information, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information.
The vehicle simulation device is specifically understood to be a simulation device consisting of a simulated cockpit and a multi-degree-of-freedom running system. The simulated cockpit adopts a racing car seat, supports the adjustable chair back and the front and back position adjustment, five operating parts adopt simulated car solid part configuration, and a steering mechanism is constructed by adopting a steering mechanism assembly with moment feedback, so that the functions of tyre feedback moment and automatic centering during steering are realized. The gear operating mechanism adopts a real vehicle gear shifting sheet, has real vehicle sense, and operating parts such as an accelerator, a clutch, a brake and the like are composed of mechanical parts of a simulation vehicle and are integrated on a set of components. The multi-degree-of-freedom running system consists of four servo lead screws, a double-layer supporting platform and a servo driving control system. The servo screw column is arranged on a fixed base on the ground, the supporting platform is formed by welding double-layer steel plates, the two steel plates are fastened by bolts, and the servo drive control system realizes the motion of three degrees of freedom of the moving platform by controlling the travel of the electric cylinder, namely the vertical translational motion in a Cartesian coordinate system and the rolling and pitching motion around XY coordinate axes. The first driving data can be specifically understood as data provided by the vehicle simulation equipment, is acquired by corresponding simulation mechanical components in the vehicle simulation equipment, and can comprise steering wheel angle data of the vehicle simulation equipment; the second driving data may be specifically understood as being obtained by simulation of a vehicle dynamics model, and may include, but is not limited to, vehicle driving speed data, vehicle yaw angle data, vehicle coordinate information, and the like.
Specifically, in the simulation process, the first running data acquired by the vehicle simulation equipment running on the virtual simulation road and the second running data of the vehicle dynamics model form the running data at the current moment. The first running data and the second running data are respectively configured with a time stamp, and the first running data and the second running data with matched time stamps are combined to obtain the running data at the current moment, wherein the time stamp matching can be that the difference value of the time stamps is smaller than an error threshold value. And transmitting the acquired current running data to an automatic driving algorithm module, wherein the automatic driving algorithm module determines predicted running data of the vehicle on the virtual simulation road according to the current running data, namely the next-time vehicle running data, and generates vehicle control information according to the predicted running data, and the vehicle control information controls the vehicle simulation equipment to perform automatic driving simulation. Or, the vehicle control information is input into a vehicle dynamics model, and the vehicle dynamics model is controlled to run on the virtual simulation road according to the vehicle control information.
The test structure schematic diagram of the simulated driving device shown in fig. 2 includes a real-time platform, a desktop computer and a vehicle simulation device, wherein the real-time platform is used for simulating a cockpit, so that a driver CAN input virtual environment data, driving data and other information, the real-time platform also includes a vehicle model, a vehicle output module outputs a vehicle through a CAN signal, the desktop computer also includes a device equivalent to a driving simulation server, the device is used for constructing a virtual environment road, simulating the vehicle posture, and sending the constructed virtual environment data to the vehicle simulation device, the driving simulation service adopts an industry high-level high-performance server as a carrier, runs the whole same-element simulation test platform software system, is provided with a high-performance GPU, generates three-dimensional images based on a high-performance computing engine in real time, supports three-screen linkage, has a rendering speed of not lower than 60 frames/second, realizes realistic simulation of various roads and environment scenes of the vehicle simulation, and in addition, CAN simulate the output of a part close to real working conditions by matching with the high-performance real-time simulation lower computer for the whole vehicle dynamic part, and CAN meet the part of hardware in-loop test requirements; the vehicle simulation equipment is used for simulating functions of pitching, cornering, swaying and the like of the vehicle. The input information is transmitted to the vehicle model, the vehicle control information is output, the driving simulation server is used for constructing a virtual running environment, and the constructed picture is transmitted to the vehicle simulation device.
Optionally, the method further comprises: forming a simulation path based on a starting point and an end point in the virtual simulation road, and sequentially setting a reference point and index information of the reference point for the simulation path; correspondingly, the current time driving data comprise the current time vehicle position information, the current time vehicle speed information, the current time vehicle yaw angle data, the current time vehicle steering wheel corner data, the index information of the nearest reference point of the last time vehicle position and the position set of the reference point in the simulation path. The index information of the nearest reference point of the vehicle position at the previous moment can be determined based on the position set of the reference point in the simulation path and the vehicle position information at the previous moment, the position set of the reference point in the simulation path is prestored, and the predicted running data comprises reference path information corresponding to a predicted time step, vehicle speed information at the next moment, vehicle yaw angle data at the next moment, vehicle steering wheel angle data at the next moment and index information of the nearest reference point at the current moment.
The reference point can be specifically understood as any point on the virtual simulation path, the simulation path can be segmented according to a preset segmentation rule, the connection part between the segments is used as a node, and each node is numbered according to the path sequence from the starting point in the virtual simulation road to serve as a reference point. The index information of the reference point may be specifically understood as a number of the reference point, that is, an index value, and an exemplary method includes setting a starting point in a virtual simulation road as one reference point and setting a corresponding index value to 0, where a segmentation rule may be divided according to a rule with equal spacing, or may be segmented according to road feature information with different spacing, and exemplary method includes setting a spacing at a curve to be smaller than a spacing at a non-curve. The position information of the nearest reference point can be matched with the position information of each reference point according to the vehicle position information at the current moment, and the reference point corresponding to the nearest index value is determined. The predicted time step may be specifically understood as a difference between the current time and the next predicted time, which corresponds to a predicted control period and may be set according to actual requirements.
Specifically, starting with a starting point in a constructed virtual simulation road, ending with an ending point, generating a corresponding simulation path through a path generating module, setting each reference point in the simulation path and index information of the reference point, wherein the required running data at the current moment comprises vehicle position information at the current moment, vehicle speed information at the current moment, vehicle yaw angle data at the current moment, vehicle steering wheel corner data at the current moment, index information of the nearest reference point of the vehicle position at the last moment and a position set of the reference points in the simulation path, and setting an index value corresponding to the index information of the nearest reference point to be 1 in an initial state; the automatic driving algorithm module determines predicted driving data of the vehicle on the virtual simulation road according to the driving data at the current moment, and correspondingly, the required predicted driving data comprises reference path information corresponding to a predicted time step, vehicle speed information at the next moment, vehicle yaw angle data at the next moment, vehicle steering wheel corner data at the next moment and index information of the nearest reference point at the current moment.
In some embodiments, the vehicle speed information in the simulation process is preset average speed data, and the vehicle yaw angle data comprises a preset yaw angle corresponding to each reference point; the steering wheel angle data of the vehicle comprises preset steering wheel angle data corresponding to each reference point; the determination mode of the reference path information corresponding to the predicted time step comprises the following steps: and determining reference index information corresponding to a preset time step after index information of the nearest reference point of the vehicle position at the last moment, and taking the reference point position information corresponding to the reference index information as reference path information corresponding to the predicted time step.
Specifically, in the simulation process, the vehicle is set to travel at a constant speed according to a preset speed, position information of the vehicle at the current moment is obtained, the position information comprises an x-axis coordinate and a y-axis coordinate of the current position information of the vehicle, an index value of a coordinate point closest to a reference track of the current vehicle is obtained, the index value is 0 in an initial state, after the index value S of the coordinate point closest to the reference track of the current vehicle is determined, a reference track point of a preset time step T is obtained, namely a reference estimated point of the index value between (S, S+T) is obtained, the current index value S is 1, then reference track coordinate points of the 2 nd, 3 rd, … … th and 1+T are obtained, the distance between the current position of the vehicle and the coordinate points of the reference track is calculated, the index value of the coordinate points closest to the reference track is updated, the index value of the coordinate point closest to the reference track is used as output of a path module, and reference path information, vehicle speed reference information and vehicle control corner reference information of future coordinate points are output after the index value of the coordinate points closest to the reference track. For example, when the current index value is 1, information such as reference paths of the 2 nd, 3 rd, … … th, and 1 st+t coordinate points is used as the output of the module.
In some embodiments, determining vehicle control information based on predicted travel data includes: and performing simulation processing on the predicted running data based on the MPC algorithm to obtain vehicle control information, wherein the vehicle control information comprises a vehicle front wheel deflection angle at the next moment, vehicle position information at the next moment, vehicle speed at the next moment, a vehicle yaw angle at the next moment, a steering wheel corner at the next moment and vehicle acceleration at the next moment.
Among them, MPC (Model Predictive Control ) is a multivariable control strategy for controlling a process in a further control mode when certain constraints are satisfied in the process control. The MPC is based on a dynamic model of a process, and predicts the corresponding dependent variable change of a modeled system when the independent variable changes through a linear empirical model obtained by system identification. The MPC is characterized by optimizing each time for a current time block, then optimizing for a next time block, predicting future events and performing corresponding processing. The MPC algorithm constructs a cost function according to the input state information of the vehicle and the predicted running data, is used for calculating errors of the predicted control data and the reference data, performs optimization solution by calling an external optimization library, and finally obtains a predicted result through rolling calculation and feedback correction.
Specifically, the scroll calculating process is such that the control amount information data calculated from the vehicle state at the present time is executed only at the present time. And (4) re-solving the next time of data input sampling again to obtain a new set of optimal results. The calculation process converts the performance requirement and the constraint of the system into the value of the objective function and the value range of the solution of the optimization problem respectively, so as to obtain the optimal control quantity sequence in the future control time domain.
Specifically, in the feedback correction process, simulation processing is performed on predicted running data through an MPC algorithm, reference information of the predicted running data is used for constructing an objective function and optimizing and solving, and errors actually output by the reference information and a model are used as a part of the objective function. And ensuring that the actual output result approaches to the reference value by optimizing and solving the minimum value of the objective function. And returning the first control quantity of the optimization solution to the control system, and influencing the motion state of the next period model. In the whole calculation process, not only future reference data information is needed to be used as feedforward compensation, but also feedback compensation is needed to be performed based on the current system state. And finally, transmitting the prediction control information to a driver model, namely transmitting the generated prediction control information such as the front wheel deflection angle of the vehicle at the next moment, the vehicle position information at the next moment, the vehicle speed at the next moment, the vehicle yaw angle at the next moment, the steering wheel rotation angle at the next moment, the vehicle acceleration at the next moment and the like to the driver model.
On the basis of the above embodiment, the method further includes: and acquiring environmental data of the vehicle dynamics model in the running process of the vehicle dynamics model on the virtual simulation road through a simulation sensor configured on the vehicle dynamics model, and detecting abnormal running of the vehicle dynamics model based on the environmental data.
The simulation sensor specifically comprises a simulation radar, a simulation camera and the like, and is used for detecting running environment data of a vehicle dynamics model. The driving environment data refers to environment data during driving of the vehicle dynamics model on the virtual simulation road.
Specifically, a simulation sensor is configured on a vehicle dynamics model, environmental data of the vehicle dynamics model in the running process on a virtual simulation road is obtained through the simulation sensor, the obtained environmental data is identified, whether the vehicle dynamics model runs according to predictive control information in the running process is determined, and whether the vehicle dynamics model has a line pressing or an abnormality deviating from a preset running track is detected.
On the basis of the above embodiment, the method further includes: acquiring driving data of a vehicle dynamics model and driving data of vehicle simulation equipment in a simulation process; in the case where there is a difference between the running data of the vehicle dynamics model and the running data of the vehicle simulation apparatus, difference comparison data is generated based on the running data of the vehicle dynamics model and the running data of the vehicle simulation apparatus, the difference comparison data being used to optimize the vehicle dynamics model.
Specifically, vehicle control information is respectively input into a vehicle dynamics model and vehicle simulation equipment as input parameters, the vehicle dynamics model carries out simulation running according to the vehicle control information to obtain running data corresponding to the vehicle dynamics model, the vehicle simulation equipment carries out simulation running according to the vehicle control information to obtain the running data corresponding to the vehicle simulation equipment, further, the obtained running data of the vehicle dynamics model and the running data of the vehicle simulation equipment are compared to judge whether a difference exists, if the difference exists, difference comparison data are generated based on the running data of the vehicle dynamics model and the running data of the vehicle simulation equipment, the obtained difference comparison data are returned to the vehicle dynamics model to be used for optimizing the vehicle dynamics model until the difference comparison data tend to be stable or meet a preset difference threshold value, and the optimized vehicle dynamics model is obtained.
According to the technical scheme, virtual environment data and a vehicle dynamics model are rendered, wherein the virtual environment data comprises a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road; automatic driving simulation is carried out based on the virtual simulation road, and in the simulation process: the method comprises the steps of obtaining current time running data of the vehicle simulation equipment, determining predicted running data of the vehicle on the virtual simulation road based on the current time running data, determining vehicle control information based on the predicted running data, controlling the vehicle simulation equipment to conduct automatic driving simulation based on the vehicle control information, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information.
Fig. 3 is a schematic structural diagram of an autopilot simulation system according to an embodiment of the present invention. As shown in fig. 3, the system includes: a simulation test system 310, a traffic scenario server 320, and a vehicle simulation device 330.
Wherein the simulation test system 310 includes a road construction module 3101, an autopilot processing module 3102, and a vehicle dynamics model 3103; the traffic scene server 320 pre-stores virtual environment data; a road construction module 3101 for constructing a virtual simulation road in the virtual environment data; the autopilot processing module 3102 is configured to render virtual environment data and a vehicle dynamics model 3103 on a virtual simulation road, and perform autopilot simulation based on the virtual simulation road, where in the simulation process: acquiring first traveling data of the vehicle simulation device 330 and second traveling data of the vehicle dynamics model, wherein the first traveling data and the second traveling data form current-time traveling data, determining predicted traveling data of the vehicle on the virtual simulation road based on the current-time traveling data, determining vehicle control information based on the predicted traveling data, transmitting the vehicle control information to the vehicle simulation device 330, and/or controlling the vehicle dynamics model 3103 to travel on the virtual simulation road based on the vehicle control information; the vehicle simulation apparatus 330 is provided with a running response means for executing vehicle control information to perform an automatic driving simulation.
Specifically, virtual environment data is pre-stored in the traffic scene server 320 of the simulation test system 310, a virtual simulation road is built in the virtual environment data through the road building module 3101, and then the virtual environment data and the vehicle dynamics model 3103 on the virtual simulation road are rendered by the automatic driving processing module 3102 by adopting a rendering technology, so that a visualized simulation road is obtained, and the rendered vehicle dynamics model 3103 performs automatic driving simulation on the virtual simulation road. In the simulation process, a data reading request may be sent to the vehicle simulation device 330 to obtain current time running data of the vehicle simulation device 330, or the current time running data may be sent to the autopilot processing module 3102 by the vehicle simulation device 330 at a set time interval when the current time running data reaches a preset time interval, an autopilot algorithm in the autopilot processing module 3102 predicts predicted running data of the vehicle on the virtual simulation road according to the current time running data, then determines vehicle control information according to the predicted running data, sends the determined vehicle control information to the vehicle simulation device 330, and the vehicle simulation device 330 performs autopilot simulation according to the control information, or controls the running state of the vehicle dynamics model 3103 through the determined vehicle control information to run on the virtual simulation road, or directly sends the control information to the vehicle simulation device 330 and the vehicle dynamics model 3103 at the same time after the vehicle control information is determined, thereby controlling autopilot simulation of the vehicle simulation device 330 and the vehicle dynamics model 3103.
The vehicle simulation device 330 is provided with a travel response means for executing vehicle control information to perform an automatic driving simulation, and optionally, the vehicle simulation device 330 is provided with at least a degree of freedom means for pitch, yaw, and yaw.
Specifically, in the case where the vehicle simulation device 330 receives the vehicle control information, the configured travel response unit performs automatic driving simulation according to the received control information, and the control information may be executed by the degree of freedom unit configured with at least pitch, yaw, and yaw, so as to implement control operations such as vertical translational motion in a cartesian coordinate system, and roll and pitch about XY coordinate axes, which are applied to the simulation analysis of the stability and smoothness of the entire vehicle.
Based on the above embodiment, the simulation test system 310 further includes a sensor module 3104, where the sensor module 3104 is configured to acquire environmental data of the vehicle dynamics model 3103 during the running on the virtual simulation road, and send the environmental data to the autopilot processing module 3102; the automatic driving processing module 3102 performs abnormal driving detection on the vehicle dynamics model 3103 based on the environmental data.
Specifically, the simulation test system 310 is further provided with a sensor module 3104, where the sensor module 3104 may include a laser radar, a millimeter wave radar, a camera, and other devices, and environmental data, which may be exemplary, acquired virtual environment image information, of the vehicle dynamics model 3103 during the running process on the virtual simulation road is acquired by the sensor module 3104. The acquired environmental data is sent to an automatic driving processing module 3102, the automatic driving processing module 3102 analyzes the environmental data, abnormal driving detection is carried out on the vehicle dynamics model 3103, whether the vehicle dynamics model 3103 has violations or not on the virtual simulation road is detected, and whether a compacting line problem exists or not is detected by way of example; alternatively, it is detected whether or not the vehicle dynamics model 3103 has a case where the execution operation differs from the vehicle control information.
According to the technical scheme, the automatic driving simulation system comprises a simulation test system, a traffic scene server and vehicle simulation equipment; the simulation test system comprises a road construction module, an automatic driving processing module and a vehicle dynamics model; the traffic scene server pre-stores virtual environment data; the road construction module constructs a virtual simulation road in the virtual environment data; the automatic driving processing module renders virtual environment data and a vehicle dynamics model on a virtual simulation road, and performs automatic driving simulation based on the virtual simulation road, wherein in the simulation process: acquiring first running data of a vehicle simulation device and second running data of a vehicle dynamics model, wherein the first running data and the second running data form current running data, determining predicted running data of a vehicle on a virtual simulation road based on the current running data, determining vehicle control information based on the predicted running data, transmitting the vehicle control information to the vehicle simulation device, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information; a vehicle simulation apparatus is provided with a travel response means for executing vehicle control information to perform an automatic driving simulation. The vehicle simulation system has the advantages that the simulation test system is utilized to carry out simulation test, the traffic scene server is utilized to store various virtual environment data, various virtual form environment data are facilitated to be determined, the vehicle is simulated by the vehicle simulation equipment, and the running response component is configured in the vehicle simulation equipment and is used for executing vehicle control information so as to carry out automatic driving simulation, so that the running state of the vehicle simulation equipment is more consistent with the running state of an actual vehicle, the simulation efficiency of the automatic driving simulation system is improved, the development test efficiency is improved, and the test cost is reduced.
Fig. 4 is a schematic structural diagram of a simulation test system according to an embodiment of the present invention. Simulation test system 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The simulated test system may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the simulation test system 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the simulation test system 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the simulation test system 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the simulation test system 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as an autopilot simulation method.
In some embodiments, the autopilot simulation method may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the simulation test system 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the autopilot simulation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the autopilot simulation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the autopilot simulation method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and the computer instructions are used for enabling a processor to execute an automatic driving simulation method, and the method comprises the following steps:
rendering virtual environment data and a vehicle dynamics model, wherein the virtual environment data comprises a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road;
automatic driving simulation is carried out based on the virtual simulation road, and in the simulation process: and acquiring first running data of the vehicle simulation equipment and second running data of the vehicle dynamics model, wherein the first running data and the second running data form current running data, determining predicted running data of the vehicle on the virtual simulation road based on the current running data, determining vehicle control information based on the predicted running data, controlling the vehicle simulation equipment to perform automatic driving simulation based on the vehicle control information, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here may be implemented on a simulation test system having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or a trackball) through which a user can provide input to the simulation test system. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (12)
1. An automated driving simulation method, comprising:
rendering virtual environment data and a vehicle dynamics model, wherein the virtual environment data comprises a virtual simulation road constructed in advance, and the vehicle dynamics model is rendered on the virtual simulation road;
and carrying out automatic driving simulation based on the virtual simulation road, wherein in the simulation process: and acquiring first running data of a vehicle simulation device and second running data of the vehicle dynamics model, wherein the first running data and the second running data form current running data, the predicted running data of the vehicle on the virtual simulation road is determined based on the current running data, vehicle control information is determined based on the predicted running data, the vehicle simulation device is controlled to conduct automatic driving simulation based on the vehicle control information, and/or the vehicle dynamics model is controlled to run on the virtual simulation road based on the vehicle control information.
2. The method of claim 1, wherein the method of creating a virtual simulation road comprises at least one of:
acquiring a simulation demand configuration file, wherein the simulation demand configuration file comprises at least one piece of road configuration information; constructing a simulation road section based on the road configuration information, and forming a virtual simulation road based on at least one simulation road section;
Detecting road construction operation in the simulation interaction page, wherein the road construction operation comprises road configuration information; constructing a simulation road section based on the road configuration information, and forming a virtual simulation road based on at least one simulation road section;
detecting road construction operation in the simulation interaction page, wherein the road construction operation comprises road configuration information; matching a target simulation road section in the constructed simulation road sections based on the road configuration information, and forming a virtual simulation road based on the target simulation road section;
in the simulation interaction page, at least one simulation road section is called from a pre-created road library in response to a road calling operation, and a virtual simulation road is formed based on at least one simulation road section.
3. The method according to claim 1, wherein the method further comprises:
forming a simulation path based on a starting point and an ending point in the virtual simulation road, and sequentially setting a reference point and index information of the reference point for the simulation path;
correspondingly, the current time driving data comprise vehicle position information at the current time, vehicle speed information at the current time, vehicle yaw angle data at the current time, vehicle steering wheel corner data at the current time, index information of the nearest reference point of the vehicle position at the last time and a position set of the reference point in the simulation path;
The predicted traveling data comprises reference path information corresponding to a predicted time step, vehicle speed information at the next moment, vehicle yaw angle data at the next moment, vehicle steering wheel angle data at the next moment and index information of the nearest reference point at the current moment.
4. A method according to claim 3, wherein the vehicle speed information during the simulation is preset average speed data, and the vehicle yaw angle data includes a preset yaw angle corresponding to each reference point; the steering wheel angle data of the vehicle comprises preset steering wheel angle data corresponding to each reference point;
the determining mode of the reference path information corresponding to the predicted time step comprises the following steps: and determining reference index information corresponding to a preset time step after the index information of the nearest reference point of the vehicle position at the previous moment, and taking the reference point position information corresponding to the reference index information as reference path information corresponding to the predicted time step.
5. The method of claim 1, wherein the determining vehicle control information based on the predicted travel data comprises:
and performing simulation processing on the predicted running data based on an MPC algorithm to obtain the vehicle control information, wherein the vehicle control information comprises a vehicle front wheel deflection angle at the next moment, vehicle position information at the next moment, vehicle speed at the next moment, a vehicle yaw angle at the next moment, a steering wheel corner at the next moment and vehicle acceleration at the next moment.
6. The method according to claim 1, wherein the method further comprises:
and acquiring environmental data of the vehicle dynamics model in the running process on the virtual simulation road through a simulation sensor configured on the vehicle dynamics model, and detecting abnormal running of the vehicle dynamics model based on the environmental data.
7. The method according to claim 1, wherein the method further comprises:
acquiring running data of the vehicle dynamics model and running data of the vehicle simulation equipment in a simulation process;
in the case where there is a difference between the running data of the vehicle dynamics model and the running data of the vehicle simulation apparatus, difference comparison data for optimizing the vehicle dynamics model is generated based on the running data of the vehicle dynamics model and the running data of the vehicle simulation apparatus.
8. An autopilot simulation system, comprising: the system comprises a simulation test system, a traffic scene server and vehicle simulation equipment; the simulation test system comprises a road construction module, an automatic driving processing module and a vehicle dynamics model;
The traffic scene server is pre-stored with virtual environment data;
the road construction module is used for constructing a virtual simulation road in the virtual environment data;
the automatic driving processing module is used for rendering virtual environment data and a vehicle dynamics model on the virtual simulation road, and performing automatic driving simulation based on the virtual simulation road, wherein in the simulation process: acquiring first running data of a vehicle simulation device and second running data of the vehicle dynamics model, wherein the first running data and the second running data form current moment running data, determining predicted running data of a vehicle on the virtual simulation road based on the current moment running data, determining vehicle control information based on the predicted running data, transmitting the vehicle control information to the vehicle simulation device, and/or controlling the vehicle dynamics model to run on the virtual simulation road based on the vehicle control information;
the vehicle simulation apparatus is provided with a travel response means for executing the vehicle control information to perform an automatic driving simulation.
9. The system of claim 8, wherein the simulation test system further comprises a sensor module for acquiring environmental data of the vehicle dynamics model during travel on the virtual simulation road and transmitting the environmental data to the autopilot processing module;
The automatic driving processing module detects abnormal driving of the vehicle dynamics model based on the environmental data.
10. The system of claim 8, wherein the vehicle simulation device is configured with at least degrees of freedom components of pitch, yaw, and yaw.
11. A simulation test system, the simulation test system comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the autopilot simulation method of any one of claims 1-7.
12. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the autopilot simulation method of any one of claims 1-7 when executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311488933.5A CN117434855A (en) | 2023-11-09 | 2023-11-09 | Automatic driving simulation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311488933.5A CN117434855A (en) | 2023-11-09 | 2023-11-09 | Automatic driving simulation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117434855A true CN117434855A (en) | 2024-01-23 |
Family
ID=89555047
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311488933.5A Pending CN117434855A (en) | 2023-11-09 | 2023-11-09 | Automatic driving simulation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117434855A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117991662A (en) * | 2024-04-03 | 2024-05-07 | 凯朴硕科技(杭州)有限公司 | Chassis control rack system of simulation new energy automobile |
-
2023
- 2023-11-09 CN CN202311488933.5A patent/CN117434855A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117991662A (en) * | 2024-04-03 | 2024-05-07 | 凯朴硕科技(杭州)有限公司 | Chassis control rack system of simulation new energy automobile |
| CN117991662B (en) * | 2024-04-03 | 2024-06-07 | 凯朴硕科技(杭州)有限公司 | Chassis control rack system of simulation new energy automobile |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2021152906A (en) | Method, device, appliance and storage medium for predicting vehicle locus | |
| CN109522673B (en) | Test method, device, equipment and storage medium | |
| JP2022524168A (en) | Methods and systems for determining the driver's intentions in steering a semi-automatic vehicle | |
| CN117434855A (en) | Automatic driving simulation method and system | |
| KR20210089116A (en) | Training method, apparatus and apparatus of longitudinal kinetic model | |
| CN112579464A (en) | Verification method, device and equipment of automatic driving algorithm and storage medium | |
| JP2023029472A (en) | Information confirming method and device, electronic apparatus, storage medium and computer program | |
| CN114475656B (en) | Travel track prediction method, apparatus, electronic device and storage medium | |
| CN115848382A (en) | Vehicle running speed control method and device, electronic equipment and storage medium | |
| CN114802251A (en) | Control method and device for automatic driving vehicle, electronic device and storage medium | |
| JP2022081613A (en) | Method, apparatus, equipment, medium and computer program for identifying characteristic of automatic operation | |
| CN112651535A (en) | Local path planning method and device, storage medium, electronic equipment and vehicle | |
| CN113978465A (en) | Lane-changing track planning method, device, equipment and storage medium | |
| CN116088538B (en) | Vehicle track information generation method, device, equipment and computer readable medium | |
| CN115454082A (en) | Vehicle obstacle avoidance method and system, computer readable storage medium and electronic device | |
| CN116105712A (en) | Road map generation method, reinjection method, computer device and medium | |
| CN115583254A (en) | Path planning method, device and equipment and automatic driving vehicle | |
| CN114282776A (en) | Method, device, equipment and medium for cooperatively evaluating automatic driving safety of vehicle and road | |
| CN109144070A (en) | Mobile device assists automatic Pilot method, automobile and storage medium | |
| CN111951552B (en) | Method and related device for risk management in automatic driving | |
| CN115171384A (en) | Key vehicle position delay compensation method and device in vehicle-mounted display process | |
| CN114537421A (en) | Method, device, equipment and computer readable medium for adjusting system fault | |
| CN116476851B (en) | Vehicle data processing method and device, electronic equipment and storage medium | |
| CN113799790B (en) | Vehicle speed control performance testing method and device, electronic equipment and medium | |
| CN116985766B (en) | Collision relief control system and method based on domain controller |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |