CN111123731A - Method, device, storage medium and terminal equipment for simulating automatic driving vehicle - Google Patents
Method, device, storage medium and terminal equipment for simulating automatic driving vehicle Download PDFInfo
- Publication number
- CN111123731A CN111123731A CN201811289352.8A CN201811289352A CN111123731A CN 111123731 A CN111123731 A CN 111123731A CN 201811289352 A CN201811289352 A CN 201811289352A CN 111123731 A CN111123731 A CN 111123731A
- Authority
- CN
- China
- Prior art keywords
- simulation
- vehicle
- driving
- drive test
- data acquisition
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides a method, a device, a storage medium and a terminal device for simulating an automatic driving vehicle, wherein the method comprises the following steps: acquiring drive test data in the driving process of a data acquisition vehicle, wherein the drive test data comprises an actual driving track of the data acquisition vehicle; reproducing a simulation environment of the autonomous vehicle using the drive test data; comparing, in the simulated environment, a deviation between a travel trajectory of the autonomous vehicle at a current travel segment and an actual travel trajectory of the data collection vehicle; and suspending the simulation if the offset exceeds a preset offset tolerance. By adopting the invention, the excessive simulation distortion can be avoided, and the utilization rate of simulation resources is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a device, a storage medium and terminal equipment for simulating an automatic driving vehicle.
Background
An automatic driving vehicle is also called as an unmanned vehicle, a computer driving vehicle or a wheeled mobile robot, and is an intelligent vehicle which realizes unmanned driving through a computer system. With the development of unmanned vehicles, the iteration speed of the control algorithm of the unmanned vehicle system is faster and faster, and the cost and the period of real vehicle debugging are greatly increased. For this purpose, a corresponding simulation environment is created for adjusting the control algorithm. In order to make the simulation environment fit the actual environment, data of surrounding driving road conditions detected by a vehicle-mounted sensor in the driving process of the actual vehicle are generally obtained, then the simulation environment is constructed by using the data of the surrounding driving road conditions, the simulation environment is used for debugging unmanned vehicles, and the automatic driving technology is subjected to iterative optimization.
However, in the simulation test process of the unmanned vehicle, because the visual range of the sensing equipment of the unmanned vehicle is limited and the unmanned vehicle does not have the penetrating capability, after the simulated main vehicle changes the control strategy or the map version, if the relative position of the simulated main vehicle and the relative position of the main vehicle at the actual road time change greatly, the sensing result is distorted, and the effectiveness of the field shadow is greatly reduced.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a storage medium, and a terminal device for simulating an autonomous vehicle, so as to solve or alleviate one or more of the above technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a method for simulating an autonomous vehicle, including:
acquiring drive test data in the driving process of a data acquisition vehicle, wherein the drive test data comprises an actual driving track of the data acquisition vehicle;
reproducing a simulation environment of the autonomous vehicle using the drive test data;
comparing, in the simulated environment, a deviation between a travel trajectory of the autonomous vehicle at a current travel segment and an actual travel trajectory of the data collection vehicle; and
if the offset exceeds a preset offset tolerance, suspending the simulation.
In one embodiment, the method further comprises:
and restarting the simulation after the running state of the automatic driving vehicle on the current running road section is restored to the actual running state of the data acquisition vehicle on the current running road section.
In one embodiment, the driving state includes at least one of a driving position, a driving speed, a driving heading angle, and a driving decision parameter.
In one embodiment, the method further comprises:
determining the weather condition of the simulation environment by using the drive test data; and
and determining the deviation tolerance according to the weather condition.
In one embodiment, the method further comprises:
acquiring the sensitivity of a data collector of the data collection vehicle; and
and determining the offset tolerance according to the sensitivity.
In a second aspect, an embodiment of the present invention provides an apparatus for simulating an autonomous vehicle, including:
the drive test data acquisition module is used for acquiring drive test data in the driving process of a data acquisition vehicle, wherein the drive test data comprises the actual driving track of the data acquisition vehicle;
a simulation reproduction module for reproducing a simulation environment of the autonomous vehicle using the drive test data;
a simulation deviation comparison module for comparing a deviation between a running track of the autonomous vehicle on a current running road section and an actual running track of the data acquisition vehicle in the simulation environment; and
and the simulation suspending module is used for suspending simulation if the deviation exceeds the preset deviation tolerance.
In one embodiment, the apparatus further comprises:
and the simulation restarting module is used for restarting simulation after the driving state of the automatic driving vehicle on the current driving road section is restored to the actual driving state of the data acquisition vehicle on the current driving road section.
In one embodiment, the driving state includes at least one of a driving position, a driving speed, a driving heading angle, and a driving decision parameter.
In one embodiment, the apparatus further comprises:
the weather condition determining module is used for determining the weather condition of the simulation environment by utilizing the drive test data; and
and the first tolerance setting module is used for determining the offset tolerance according to the weather condition.
In one embodiment, the apparatus further comprises:
the sensitivity acquisition module is used for acquiring the sensitivity of a data acquisition unit of the data acquisition vehicle;
and the second tolerance setting module is used for determining the offset tolerance according to the sensitivity.
In a third aspect, an embodiment of the present invention provides a device for simulating an autonomous vehicle, where functions of the device may be implemented by hardware, or may be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible design, the structure of the autonomous vehicle simulation includes a processor and a memory, the memory is used for the device of the autonomous vehicle simulation to execute the program of the autonomous vehicle simulation, and the processor is configured to execute the program stored in the memory. The apparatus for autonomous vehicle simulation may further include a communication interface for communicating the apparatus for autonomous vehicle simulation with other devices or a communication network.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium for computer software instructions for an apparatus for autonomous vehicle simulation, including a program for performing the method for autonomous vehicle simulation described above.
Any one of the above technical solutions has the following advantages or beneficial effects:
in the embodiment of the invention, in the simulation environment of the automatic driving vehicle reproduced by using the drive test data, the deviation between the running track of the automatic driving vehicle on the current running road section and the actual running track of the actual data acquisition vehicle is compared aiming at each moment, and if the deviation exceeds the preset deviation tolerance, the simulation is suspended so as to avoid the excessive distortion of the subsequent simulation.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
FIG. 1 is a schematic flow chart diagram illustrating one embodiment of a method for automated driving vehicle simulation provided by the present invention;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a method for automated driving vehicle simulation provided by the present invention;
FIG. 3 is a flow chart illustrating one embodiment of a process for adjusting the skew tolerance provided by the present invention;
FIG. 4 is a flow chart illustrating another embodiment of a process for adjusting the offset tolerance provided by the present invention;
FIG. 5 is a schematic diagram illustrating an embodiment of an apparatus for simulating an autonomous vehicle according to the present invention;
FIG. 6 is a schematic diagram illustrating an embodiment of an apparatus for simulating an autonomous vehicle according to the present invention;
fig. 7 is a schematic structural diagram of an embodiment of a terminal device provided by the present invention.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
Referring to fig. 1, an embodiment of the invention provides a method for simulating an autonomous vehicle. The embodiment includes steps S100 to S400, which are specifically as follows:
s100, acquiring drive test data in the driving process of the data acquisition vehicle, wherein the drive test data comprises an actual driving track of the data acquisition vehicle.
The drive test data may include driving data of the vehicle and all obstacles, which can be acquired by the sensors of the data acquisition vehicle, traffic road environment, and the like. The obstacles may include pedestrians, obstacle vehicles, and the like.
S200, reproducing the simulation environment of the automatic driving vehicle by using the drive test data.
Because the drive test data can include the drive test data of the simulation targets of the data acquisition vehicles, pedestrians and various obstacle vehicles, according to the acquisition time sequence of the data acquisition vehicles, the organization form of the acquired drive test data of the simulation targets is as follows: d ═ Dt1,Dt2,…,DtnWhere i ═ 1,2, …, n, DtiAnd collecting the data measured by the vehicle for the data at the moment ti. DtiTissue form Dti＝{Dti,a1,Dti,a2,…,Dti,akWhere j is 1,2, …, k. Dti,ajThe data is the data of the jth simulation target at time ti.
In the simulation, because the clock is not synchronous, the time of each simulation frame and the time of each drive test data frame are not completely overlapped, and the operations such as data smoothing and interpolation can be performed, the data organization form used by the simulation is a spatial sequence, namely, D ═ Da1,Da2,…,DamIn which D isapFor the data of the pth simulation target, p is 1,2, …, m. For each of the simulated target data at each time instant,it has an organization form Dap＝{Dap,t1,Dap,t2,…,Dap,tsIn which D isap,tqFor the data of the pth simulation target at the qth time, q is 1,2, …, s.
Thus, D ═ D can be utilizeda1,Da2,…,DamAnd reproducing the running track of each simulation target in the simulation environment. Meanwhile, the automatic driving vehicle (simulated data acquisition vehicle) can determine the driving state of each obstacle vehicle at the next moment by using the running strategy of the automatic driving vehicle and the driving state of each obstacle vehicle. Namely, in the normal simulation process, the automatic driving vehicle runs in an automatic driving mode, and the obstacle vehicle runs in a fixed track mode.
S300, in the simulation environment, comparing the running track of the automatic driving vehicle on the current running road section with the deviation between the actual running track of the data acquisition vehicle.
In some embodiments, at each time the autonomous vehicle is operating in the simulated environment, the travel trajectory of the travel segment in which the autonomous vehicle is currently located may be determined and compared to the actual trajectory of the data-capturing vehicle in the same travel segment, e.g., whether the trajectory lines are consistent, whether the status of the vehicle is consistent, etc. The state of the vehicle may include straight ahead, cornering, lane changing, following a preceding vehicle, etc. Of course, the state of the vehicle may be clutch opening, brake opening, steering wheel angle, or the like, or a specific position of the vehicle in the travel route, travel speed, or the like. Therefore, the embodiment of the invention can judge whether the deviation of the running track exceeds the tolerance or not from multiple dimensions.
S400, if the deviation exceeds the preset deviation tolerance, the simulation is suspended.
For example, if the distance between the driving position of the autonomous vehicle and the driving position of the data collection vehicle is more than 80 meters in the same road section, the deviation tolerance of the user is considered to be exceeded. If such a condition is met, the simulation may be considered distorted and the distortion exceeds a tolerable level, and the simulation may be suspended. The simulation is not continuously carried out according to the current simulation state, so that the simulation efficiency can be improved, and the meaningless simulation is avoided under the distortion condition.
In the embodiment of the invention, in the simulation environment of the automatic driving vehicle reproduced by using the drive test data, the deviation between the running track of the automatic driving vehicle on the current running road section and the actual running track of the actual data acquisition vehicle is compared aiming at each moment, and if the deviation exceeds the preset deviation tolerance, the simulation is suspended, so that the excessive distortion of the subsequent simulation is avoided, and the utilization rate of simulation resources is improved.
In some embodiments, as shown in fig. 2, the method for simulating an autonomous vehicle provided in this embodiment may further include:
and S500, restarting the simulation after the running state of the automatic driving vehicle on the current running road section is restored to the actual running state of the data acquisition vehicle on the current running road section.
Because the simulation scene and the simulation effect before the current driving road section are fit with the actual environment, the driving condition of the road section before the current driving road section can be continuously simulated from the current road section without re-simulation, thereby improving the simulation efficiency. In order to reduce the distortion degree of the subsequent simulation and not exceed the offset tolerance of the user, the running state of the automatic driving vehicle on the current running road section can be restored to the actual running state of the data acquisition vehicle on the current running road section.
In some embodiments, the driving conditions may include at least one of: the driving position, the driving speed, the driving course angle, the driving decision parameter and the like.
In the embodiment of the invention, whether the deviation of the simulated driving track exceeds the tolerance or not can be judged in multiple dimensions. If the data exceeds the preset threshold value, the simulation scene can be dynamically cut off, and the simulation is restarted after the simulated data is restored to the data of the original data acquisition vehicle at the moment. Therefore, the restoration of a real scene and the iteration of a closed-loop algorithm based on the restoration can be supported.
Closed loop means that the control decision of the algorithm can be used as the input of a power system of the automatic driving vehicle to influence the motion of the automatic driving vehicle, and the change of the motion state of the automatic driving vehicle can influence the control decision of the algorithm to form a closed loop, which is different from an open loop. The control decision of the open loop algorithm is based on the running state of the automatic driving vehicle, but the control decision is not input into a power system and does not influence the motion of the vehicle, and the motion state of the vehicle plays back data recorded in the route measurement.
The closed-loop algorithm iteration refers to that under a closed-loop state, problems (such as collision risks, stagnation and the like) existing in the algorithm are found, after the algorithm is optimized, verification is carried out in simulation to see whether the problems are solved, if not, optimization is continued, and iteration is carried out repeatedly until the problems are solved.
In the embodiment of the invention, the tolerance can be flexibly adjusted according to actual conditions. Thus, in some embodiments, the process of adjusting the offset tolerance, as shown in fig. 3, may include:
and S610, determining the weather condition of the simulation environment by using the drive test data.
The drive test data can comprise the real environment where the data acquisition vehicle shot by the automobile data recorder is located. If the captured image includes dense water that falls down, it can be determined that it is raining at the present time. If the captured image includes snowfield with white flowers, it can be determined that snow exists on the road of the current road section. If the captured image includes light but the front is dark, it can be determined that the current time is at night. The image can be identified by using the identification model, and the weather condition of the simulation environment can be obtained. The recognition model can be generated by training according to a large number of sample images and training data marked on weather conditions of the sample patterns. And determining the weather condition of the simulation environment according to the identified weather condition, and then restoring according to the determined weather condition in the process of restoring the simulation environment. Wherein, the identification model is used for identifying the weather condition, so that the identification accuracy and efficiency can be improved.
And S620, determining the offset tolerance according to the weather condition. The worse the weather, the less tolerant to drift.
For example, if in the case of rainy or snowy weather, or at night, etc., the distance of tolerance of the offset position on the same road section can be appropriately shortened. If under sunny, daytime, etc., the tolerance distance of the offset position at the same road section can be appropriately lengthened.
In some embodiments, the offset tolerance may also be determined based on the sensitivity of the data collector of the data collection vehicle. Specifically, as shown in fig. 4, the setting process of the offset tolerance may include steps S710 and S720 as follows:
and S710, acquiring the sensitivity of a data acquisition device of the data acquisition vehicle. The data collector can comprise sensors such as a driving recorder, a speed detector and a gyroscope.
And S720, determining the offset tolerance according to the sensitivity. The higher the sensitivity, the less offset tolerant.
The embodiment of the invention can flexibly adjust the offset tolerance, so that the simulation environment is more fit with the real environment.
Referring to fig. 5, an embodiment of the present invention provides an apparatus for simulating an autonomous vehicle, including:
the drive test data acquisition module 100 is configured to acquire drive test data of a data acquisition vehicle in a driving process, where the drive test data includes an actual driving track of the data acquisition vehicle;
a simulation reproduction module 200 for reproducing a simulation environment of the autonomous vehicle using the drive test data;
a simulation deviation comparison module 300 for comparing a deviation between a driving trajectory of the autonomous vehicle and an actual driving trajectory of the data collection vehicle in the current driving road section in the simulation environment; and
a simulation suspending module 400 configured to suspend the simulation if the deviation exceeds a preset deviation tolerance.
In one embodiment, the apparatus further comprises:
and the simulation restarting module 500 is used for restarting simulation after the driving state of the automatic driving vehicle on the current driving road section is restored to the actual driving state of the data acquisition vehicle on the current driving road section.
In one embodiment, the driving state includes at least one of a driving position, a driving speed, a driving heading angle, and a driving decision parameter.
In one embodiment, as shown in fig. 6, the apparatus further comprises:
a weather condition determining module 600, configured to determine a weather condition of the simulation environment by using the drive test data; and
a first tolerance setting module 700 configured to determine the deviation tolerance according to the weather condition.
In one embodiment, the apparatus as shown in fig. 6 further comprises:
a sensitivity acquisition module 800, configured to acquire sensitivity of a data acquisition device of the data acquisition vehicle;
a second tolerance setting module 900, configured to determine the shift tolerance according to the sensitivity.
The functions of the device can be realized by hardware, and can also be realized by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible design, the structure of the autonomous vehicle simulation includes a processor and a memory, the memory is used for the device of the autonomous vehicle simulation to execute the program of the autonomous vehicle simulation in the first aspect, and the processor is configured to execute the program stored in the memory. The apparatus for autonomous vehicle simulation may further include a communication interface for communicating the apparatus for autonomous vehicle simulation with other devices or a communication network.
An embodiment of the present invention further provides a terminal device for simulating an autonomous vehicle, as shown in fig. 7, where the terminal device includes: a memory 21 and a processor 22, the memory 21 having stored therein computer programs that may be executed on the processor 22. The processor 22, when executing the computer program, implements the method of autonomous vehicle simulation in the above-described embodiments. The number of the memory 21 and the processor 22 may be one or more.
The apparatus further comprises:
a communication interface 23 for communication between the processor 22 and an external device.
The memory 21 may include a high-speed RAM memory and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 21, the processor 22 and the communication interface 23 are implemented independently, the memory 21, the processor 22 and the communication interface 23 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 21, the processor 22 and the communication interface 23 are integrated on a chip, the memory 21, the processor 22 and the communication interface 23 may complete mutual communication through an internal interface.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The computer readable media of embodiments of the present invention may be computer readable signal media or computer readable storage media or any combination of the two. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
In embodiments of the present invention, a computer readable signal medium may comprise 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, input method, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the preceding.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments are programs that can be executed by associated hardware through instructions of the programs, and the programs can be stored in a computer readable storage medium, and when executed, comprise one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (12)
1. A method of autonomous vehicle simulation, comprising:
acquiring drive test data in the driving process of a data acquisition vehicle, wherein the drive test data comprises an actual driving track of the data acquisition vehicle;
reproducing a simulation environment of the autonomous vehicle using the drive test data;
comparing, in the simulated environment, a deviation between a travel trajectory of the autonomous vehicle at a current travel segment and an actual travel trajectory of the data collection vehicle; and
if the offset exceeds a preset offset tolerance, suspending the simulation.
2. The method of claim 1, further comprising:
and restarting the simulation after the running state of the automatic driving vehicle on the current running road section is restored to the actual running state of the data acquisition vehicle on the current running road section.
3. The method of claim 1, wherein the driving status comprises at least one of a driving location, a driving speed, a driving heading angle, and a driving decision parameter.
4. The method of any of claims 1 to 3, further comprising:
determining the weather condition of the simulation environment by using the drive test data; and
and determining the deviation tolerance according to the weather condition.
5. The method of any of claims 1 to 3, further comprising:
acquiring the sensitivity of a data collector of the data collection vehicle; and
and determining the offset tolerance according to the sensitivity.
6. An apparatus for automated driving vehicle simulation, comprising:
the drive test data acquisition module is used for acquiring drive test data in the driving process of a data acquisition vehicle, wherein the drive test data comprises the actual driving track of the data acquisition vehicle;
a simulation reproduction module for reproducing a simulation environment of the autonomous vehicle using the drive test data;
a simulation deviation comparison module for comparing a deviation between a running track of the autonomous vehicle on a current running road section and an actual running track of the data acquisition vehicle in the simulation environment; and
and the simulation suspending module is used for suspending simulation if the deviation exceeds the preset deviation tolerance.
7. The apparatus of claim 6, further comprising:
and the simulation restarting module is used for restarting simulation after the driving state of the automatic driving vehicle on the current driving road section is restored to the actual driving state of the data acquisition vehicle on the current driving road section.
8. The apparatus of claim 6, wherein the driving condition comprises at least one of a driving location, a driving speed, a driving heading angle, and a driving decision parameter.
9. The apparatus of any of claims 6 to 8, further comprising:
the weather condition determining module is used for determining the weather condition of the simulation environment by utilizing the drive test data; and
and the first tolerance setting module is used for determining the offset tolerance according to the weather condition.
10. The apparatus of any of claims 6 to 8, further comprising:
the sensitivity acquisition module is used for acquiring the sensitivity of a data acquisition unit of the data acquisition vehicle;
and the second tolerance setting module is used for determining the offset tolerance according to the sensitivity.
11. A terminal device for autonomous vehicle simulation, the terminal device comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-5.
12. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811289352.8A CN111123731B (en) | 2018-10-31 | 2018-10-31 | Method, device, storage medium and terminal equipment for simulating automatic driving vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811289352.8A CN111123731B (en) | 2018-10-31 | 2018-10-31 | Method, device, storage medium and terminal equipment for simulating automatic driving vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111123731A true CN111123731A (en) | 2020-05-08 |
| CN111123731B CN111123731B (en) | 2022-12-02 |
Family
ID=70485698
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811289352.8A Active CN111123731B (en) | 2018-10-31 | 2018-10-31 | Method, device, storage medium and terminal equipment for simulating automatic driving vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111123731B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112836395A (en) * | 2021-03-10 | 2021-05-25 | 北京车和家信息技术有限公司 | Vehicle driving data simulation method and device, electronic equipment and storage medium |
| CN113761701A (en) * | 2020-09-11 | 2021-12-07 | 北京京东乾石科技有限公司 | Method and device for target simulation control |
| CN113792410A (en) * | 2021-08-11 | 2021-12-14 | 武汉光庭信息技术股份有限公司 | Method and system for mapping vehicle control data to simulation environment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105487551A (en) * | 2016-01-07 | 2016-04-13 | 谭圆圆 | Unmanned aerial vehicle-based spray sprinkling control method and control device |
| CN106020203A (en) * | 2016-07-15 | 2016-10-12 | 百度在线网络技术（北京）有限公司 | Method and device for controlling unmanned vehicle |
| CN106198049A (en) * | 2016-07-15 | 2016-12-07 | 百度在线网络技术（北京）有限公司 | Real vehicles is at ring test system and method |
| CN107678306A (en) * | 2017-10-09 | 2018-02-09 | 驭势（上海）汽车科技有限公司 | Dynamic scene information is recorded and emulation back method, device, equipment and medium |
| CN107991898A (en) * | 2016-10-26 | 2018-05-04 | 法乐第(北京)网络科技有限公司 | A kind of automatic driving vehicle simulating test device and electronic equipment |
| CN108267322A (en) * | 2017-01-03 | 2018-07-10 | 北京百度网讯科技有限公司 | The method and system tested automatic Pilot performance |
| CN108549366A (en) * | 2018-05-04 | 2018-09-18 | 同济大学 | Intelligent automobile road driving mapping experiment method parallel with virtual test |
| US20180275658A1 (en) * | 2017-03-23 | 2018-09-27 | DeepScale, Inc. | Data synthesis for autonomous control systems |
-
2018
- 2018-10-31 CN CN201811289352.8A patent/CN111123731B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105487551A (en) * | 2016-01-07 | 2016-04-13 | 谭圆圆 | Unmanned aerial vehicle-based spray sprinkling control method and control device |
| CN106020203A (en) * | 2016-07-15 | 2016-10-12 | 百度在线网络技术（北京）有限公司 | Method and device for controlling unmanned vehicle |
| CN106198049A (en) * | 2016-07-15 | 2016-12-07 | 百度在线网络技术（北京）有限公司 | Real vehicles is at ring test system and method |
| CN107991898A (en) * | 2016-10-26 | 2018-05-04 | 法乐第(北京)网络科技有限公司 | A kind of automatic driving vehicle simulating test device and electronic equipment |
| CN108267322A (en) * | 2017-01-03 | 2018-07-10 | 北京百度网讯科技有限公司 | The method and system tested automatic Pilot performance |
| US20180275658A1 (en) * | 2017-03-23 | 2018-09-27 | DeepScale, Inc. | Data synthesis for autonomous control systems |
| CN107678306A (en) * | 2017-10-09 | 2018-02-09 | 驭势（上海）汽车科技有限公司 | Dynamic scene information is recorded and emulation back method, device, equipment and medium |
| CN108549366A (en) * | 2018-05-04 | 2018-09-18 | 同济大学 | Intelligent automobile road driving mapping experiment method parallel with virtual test |
Non-Patent Citations (1)
| Title |
|---|
| 华一丁等: "语音泊车时滞影响及轨迹动态调整策略研究", 《工程设计学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113761701A (en) * | 2020-09-11 | 2021-12-07 | 北京京东乾石科技有限公司 | Method and device for target simulation control |
| CN112836395A (en) * | 2021-03-10 | 2021-05-25 | 北京车和家信息技术有限公司 | Vehicle driving data simulation method and device, electronic equipment and storage medium |
| CN113792410A (en) * | 2021-08-11 | 2021-12-14 | 武汉光庭信息技术股份有限公司 | Method and system for mapping vehicle control data to simulation environment |
| CN113792410B (en) * | 2021-08-11 | 2024-03-08 | 武汉光庭信息技术股份有限公司 | Method and system for mapping vehicle control data to simulation environment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111123731B (en) | 2022-12-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111123735B (en) | Automatic driving simulation operation method and device | |
| CN109760675B (en) | Method, device, storage medium and terminal equipment for predicting vehicle track | |
| CN113492851B (en) | Vehicle control device, vehicle control method, and computer program for vehicle control | |
| CN111123731B (en) | Method, device, storage medium and terminal equipment for simulating automatic driving vehicle | |
| CN109849930B (en) | Method and device for calculating speed of adjacent vehicle of automatic driving automobile | |
| CN113297881B (en) | Target detection method and related device | |
| CN111123732B (en) | Method and device for simulating automatic driving vehicle, storage medium and terminal equipment | |
| CN106183979A (en) | A kind of method and apparatus vehicle reminded according to spacing | |
| CN110388929B (en) | Navigation map updating method, device and system | |
| CN110696826B (en) | Method and device for controlling a vehicle | |
| CN113435237B (en) | Object state recognition device, recognition method, and computer-readable recording medium, and control device | |
| JPWO2019065564A1 (en) | Automatic operation control device and method | |
| CN114754778B (en) | Vehicle positioning method and device, electronic equipment and storage medium | |
| CN107480592B (en) | Multi-lane detection method and tracking method | |
| CN112622923B (en) | Method and device for controlling a vehicle | |
| CN111144467A (en) | Method and system for realizing scene factor acquisition | |
| CN111123729A (en) | Method and device for vehicle driving simulation optimization, storage medium and terminal equipment | |
| CN108663368A (en) | A kind of system and method for real-time monitoring freeway network night entirety visibility | |
| CN116626670B (en) | Automatic driving model generation method and device, vehicle and storage medium | |
| CN112558036B (en) | Method and device for outputting information | |
| CN112526477B (en) | Method and device for processing information | |
| JP7147464B2 (en) | Image selection device and image selection method | |
| CN111123734A (en) | Complex scene testing method and device for unmanned vehicle and storage medium | |
| CN113574535A (en) | Training neural networks to assist driving vehicles by determining hard-to-observe bounds | |
| CN111479217B (en) | Method and system for positioning unmanned vehicle in tunnel and electronic equipment |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20211013 Address after: 105 / F, building 1, No. 10, Shangdi 10th Street, Haidian District, Beijing 100085 Applicant after: Apollo Intelligent Technology (Beijing) Co.,Ltd. Address before: 100085 Baidu Building, 10 Shangdi Tenth Street, Haidian District, Beijing Applicant before: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |