CN111159833A - Method and device for evaluating unmanned vehicle algorithm - Google Patents
Method and device for evaluating unmanned vehicle algorithm Download PDFInfo
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Abstract
The embodiment of the invention provides an evaluation method and device of an unmanned vehicle algorithm, wherein the method comprises the following steps: setting a simulation environment to enable the simulation environment to be consistent with a real environment when a driver drives a real host; running the unmanned vehicle algorithm in the simulation environment to obtain simulation running data; and comparing the simulated operation data with the real operation data of a driver when driving a real host, and evaluating the unmanned vehicle algorithm by adopting the comparison result. The method provided by the embodiment of the invention can evaluate the degree of the unmanned vehicle algorithm approaching the real driver driving level.
Description
Technical Field
The invention relates to the technical field of unmanned vehicles, in particular to an evaluation method, device and equipment of an unmanned vehicle algorithm and a computer readable storage medium.
Background
Currently, for the evaluation of the unmanned vehicle algorithm, the main implementation manner is to operate the unmanned vehicle in a simulation system and detect whether the unmanned vehicle violates the traffic rules during the simulation operation. The existing evaluation method cannot quantitatively evaluate the degree of approach of the unmanned vehicle algorithm to a skilled driver.
Disclosure of Invention
The embodiment of the invention provides an evaluation method and device for an unmanned vehicle algorithm, which are used for at least solving the technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides an evaluation method for an unmanned vehicle algorithm, including:
setting a simulation environment to enable the simulation environment to be consistent with a real environment when a driver drives a real host;
running the unmanned vehicle algorithm in the simulation environment to obtain simulation running data;
and comparing the simulated operation data with the real operation data of a driver when driving a real host, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
In one embodiment, the simulation environment includes: a simulation scene and a simulation main vehicle position;
the setting of the simulation environment comprises:
setting the simulation scene to be the same as a real scene collected by the driver when driving a real host;
setting the simulated host vehicle position to be the same as the real host vehicle position when the driver drives the real host vehicle.
In one embodiment, said comparing said simulated operational data with actual operational data of a driver driving an actual host vehicle, and using the result of said comparing to evaluate said unmanned vehicle algorithm, comprises:
and comparing at least one item of planned track data, planned speed data and planned acceleration data in the simulation operation data with a corresponding item in real operation data, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
In one embodiment, the comparing planned trajectory data in the simulated operation data with corresponding items in the real operation data, and using the result of the comparison to evaluate the unmanned vehicle algorithm includes:
aiming at more than one simulated main vehicle position, acquiring planned trajectory data of the unmanned vehicle at each simulated main vehicle position and real trajectory data of the real main vehicle at the corresponding real main vehicle position, and comparing the planned trajectory data with the real trajectory data;
and weighting and summing the comparison results to obtain an evaluation value aiming at planning track data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
In one embodiment, the comparing planned acceleration data in the simulated operation data with corresponding items in the real operation data, and using the result of the comparing to evaluate the unmanned vehicle algorithm includes:
aiming at more than one simulated main vehicle position, acquiring planned speed data of the unmanned vehicle at each simulated main vehicle position and real speed data of the real main vehicle at the corresponding real main vehicle position, and comparing the planned speed data with the real speed data;
and weighting and summing the comparison results to obtain an evaluation value aiming at planning speed data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
In one embodiment, the comparing planned acceleration data in the simulated operation data with corresponding items in the real operation data, and using the result of the comparing to evaluate the unmanned vehicle algorithm includes:
aiming at more than one simulated main vehicle position, acquiring planned acceleration data of the unmanned vehicle at each simulated main vehicle position and real acceleration data of the real main vehicle at the corresponding real main vehicle position, and comparing the planned acceleration data with the real acceleration data;
and weighting and summing the comparison results to obtain an evaluation value aiming at the planning acceleration data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
In a second aspect, an embodiment of the present invention provides an apparatus for evaluating an algorithm of an unmanned vehicle, including:
the simulation environment setting module is used for setting a simulation environment to enable the simulation environment to be consistent with a real environment of a driver when the driver drives a real host;
the simulation operation module is used for operating the unmanned vehicle algorithm in the simulation environment to obtain simulation operation data;
and the evaluation module is used for comparing the simulation operation data with the real operation data when a driver drives a real host vehicle, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
In one embodiment, the simulation environment set by the simulation environment setting module includes: a simulation scene and a simulation main vehicle position;
the simulation environment setting module includes:
the simulation scene setting submodule is used for setting the simulation scene to be the same as a real scene acquired when the driver drives a real host;
and the simulated main vehicle position setting submodule is used for setting the position of the simulated main vehicle to be the same as the real main vehicle position when the driver drives the real main vehicle.
In one embodiment, the evaluation module is configured to:
and comparing at least one item of planned track data, planned speed data and planned acceleration data in the simulation operation data with a corresponding item in real operation data, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
In one embodiment, the evaluation module comprises:
the trajectory evaluation submodule is used for acquiring planning trajectory data of the unmanned vehicle at each simulated main vehicle position and real trajectory data of the real main vehicle at the corresponding real main vehicle position according to more than one simulated main vehicle position, and comparing the planning trajectory data with the real trajectory data; and weighting and summing the comparison results to obtain an evaluation value aiming at planning track data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
In one embodiment, the evaluation module comprises:
the speed evaluation submodule is used for acquiring planning speed data of the unmanned vehicle at each simulated main vehicle position and real speed data of the real main vehicle at the corresponding real main vehicle position according to more than one simulated main vehicle position, and comparing the planning speed data with the real speed data; and weighting and summing the comparison results to obtain an evaluation value aiming at planning speed data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
In one embodiment, the evaluation module comprises:
the acceleration evaluation submodule is used for acquiring planned acceleration data of the unmanned vehicle at each simulated main vehicle position and real acceleration data of the real main vehicle at the corresponding real main vehicle position according to more than one simulated main vehicle position, and comparing the planned acceleration data with the real acceleration data; and weighting and summing the comparison results to obtain an evaluation value aiming at the planning acceleration data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
The functions can be realized by hardware, and the functions can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible design, the unmanned vehicle algorithm evaluation device includes a processor and a memory, the memory is used for storing a program of the unmanned vehicle algorithm evaluation device for executing the unmanned vehicle algorithm evaluation method in the first aspect, and the processor is configured to execute the program stored in the memory. The evaluation device of the unmanned vehicle algorithm may further comprise a communication interface for communicating the evaluation device of the unmanned vehicle algorithm with other devices or a communication network.
In a third aspect, the present invention provides a computer readable storage medium for storing computer software instructions for an unmanned vehicle algorithm evaluation device, which includes a program for executing the above unmanned vehicle algorithm evaluation method of the first aspect as an unmanned vehicle algorithm evaluation device.
One of the above technical solutions has the following advantages or beneficial effects:
according to the embodiment of the invention, the simulation environment is set to be consistent with the real environment of the driver when the real host vehicle is driven, the simulation operation data of the unmanned vehicle when running in the simulation environment is compared with the real operation data of the unmanned vehicle when running in the real host vehicle, and the obtained comparison result can reflect the difference between the unmanned vehicle algorithm and the real driver driving level, so that the unmanned vehicle algorithm is evaluated.
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.
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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 flow chart of an implementation of a method for evaluating an algorithm for an unmanned vehicle according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the implementation of step S11 in the method for evaluating the algorithm of the unmanned vehicle according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of an evaluation device for an unmanned vehicle algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an evaluation device for an unmanned vehicle algorithm according to an embodiment of the invention;
fig. 5 is a schematic diagram of an evaluation structure of an unmanned vehicle algorithm according to an embodiment of the 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.
The embodiment of the invention mainly provides an evaluation method and device of an unmanned vehicle algorithm. The following embodiments are used to describe the technical solutions.
Referring to fig. 1, fig. 1 is a flowchart of an evaluation method of an unmanned vehicle algorithm according to an embodiment of the present invention, including:
s11: and setting a simulation environment to make the simulation environment consistent with the real environment of a driver when driving a real host.
S12: and operating the unmanned vehicle algorithm in the simulation environment to obtain simulation operation data.
S13: and comparing the simulated operation data with the real operation data of a driver when driving a real host, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
In the simulation test of the unmanned vehicle, a real scene is often collected by a real main vehicle in actual operation, and the real scene is set as a simulation scene in the simulation test. And the unmanned vehicle algorithm runs in the simulation scene, and the quality of the unmanned vehicle algorithm is evaluated according to the running result.
In order to compare the difference between the unmanned vehicle algorithm and the real driver driving behavior, the simulated environment of the unmanned vehicle algorithm may be set to be completely consistent with the real environment to compare the reaction of the unmanned vehicle and the real driver in coping with the same situation. The simulation environment comprises a simulation scene and a simulation main vehicle position.
In one embodiment, as shown in fig. 2, the setting of the simulation environment in step S11 includes:
s111: the simulation scene is set to be the same as the real scene collected when the driver drives the real host.
S112: the simulated host vehicle position is set to be the same as the actual host vehicle position when the driver drives the actual host vehicle.
Here, the "scene" may be an external scene of the vehicle, including road conditions, positions of obstacles on the road, and the like; the "master position" may be the position of the vehicle itself. Therefore, when the simulation scene in the simulation environment is the same as the real scene collected by the real main vehicle, and the position of the simulation main vehicle in the simulation environment is the same as the position of the real main vehicle, the simulation environment can be ensured to be consistent with the real environment.
With respect to step S112, the real host vehicle position when the driver drives the real host vehicle may be played back during the simulation operation, and the simulated host vehicle position may be set using the played back real host vehicle position.
For step S12, the algorithm for operating the unmanned vehicle in the simulated environment may be to generate control commands for the unmanned vehicle, but not to use the control commands to control the operation of the unmanned vehicle in the simulated environment, so as to ensure that the simulated host vehicle position of the unmanned vehicle always coincides with the actual host vehicle position.
In one embodiment, step S13 includes:
and comparing at least one item of planned track data, planned speed data and planned acceleration data in the simulation operation data with a corresponding item in the real operation data, and evaluating the unmanned vehicle algorithm by adopting the comparison result. The method specifically comprises the following steps:
firstly, comparing planning track data in simulation operation data with real track data in real operation data; here, the trajectory data may be embodied as the position of the host vehicle a fixed length of time (e.g., 8 seconds) after the current time of day.
Second, the planning speed data in the simulation operation data is compared with the real speed data in the real operation data.
And thirdly, comparing the planned acceleration data in the simulation operation data with the real acceleration data in the real operation data.
In other embodiments of the present invention, other operation data can be compared in the same manner as described above, and are not listed here.
Because the vehicle continuously generates the operation data in the operation process, when the simulation operation data and the real operation data are compared, the operation data of the vehicle at each position can be selected to be compared respectively, so that a group of comparison results are obtained, and the unmanned vehicle algorithm is comprehensively evaluated.
In one embodiment, the simulation operation data and the real operation data may be extracted according to a fixed time period, and the simulation operation data and the real operation data at the corresponding time are respectively compared to obtain a comparison result.
In this embodiment, the preset time period is 1 second, and the extracting of the simulated operation data of the unmanned vehicle every 1 second includes:
1) simulating a planning track, wherein the simulation planning track can be specifically the future 8S simulation main vehicle position in the planning;
2) simulating the planning speed;
3) and simulating and planning acceleration.
Thus, at the end of the simulation, a set of data is recorded for each type of simulation run, for example:
planning trajectory data { Ps1, Ps2, Ps3 … … Psn }; wherein the content of the first and second substances,
ps1 is the planned trajectory of the unmanned vehicle recorded for the first period;
ps2 is the planned trajectory of the unmanned vehicle recorded for the second cycle;
ps3 is the planned trajectory of the unmanned vehicle recorded in the third period;
……
program speed data { Vs1, Vs2, Vs3 … … Vsn }; wherein the content of the first and second substances,
vs1 is the planned speed of the unmanned vehicle recorded for the first period;
vs2 is the planned speed of the unmanned vehicle recorded for the second cycle;
vs3 is the planned speed of the unmanned vehicle recorded for the third period;
……
planned acceleration data { As1, As2, As3 … … Asn }; wherein the content of the first and second substances,
as1 is the planned acceleration of the unmanned vehicle recorded for the first cycle;
as2 is the planned acceleration of the unmanned vehicle recorded for the second cycle;
as3 is the planned acceleration of the unmanned vehicle recorded for the third cycle;
……
for a real host vehicle driven by a skilled driver, real operation data can also be extracted according to the same period (namely 1 second), and a set of data is extracted for each kind of real operation data, for example:
real track data { Pr1, Pr2, Pr3 … … Prn }; wherein the content of the first and second substances,
pr1 is the real trajectory of a real host vehicle driven by a skilled driver recorded in the first cycle;
pr2 is the real trajectory of a real driver driven by a skilled driver recorded in the second cycle;
pr3 is the real trajectory of a real host vehicle driven by a skilled driver recorded in the third period;
……
real speed data { Vr1, Vr2, Vr3 … … Vrn }; wherein the content of the first and second substances,
vr1 is the first cycle record of the true speed of the real driver driving the truck;
vr2 is the second cycle record of the actual speed of the actual driver driving the true host vehicle;
vr3 is the third cycle record of the actual speed of the actual driver driving the true host vehicle;
……
real acceleration data { Ar1, Ar2, Ar3 … … Arn }; wherein the content of the first and second substances,
ar1 is the first cycle record of the true acceleration of a real host vehicle driven by a skilled driver;
ar2 is the second cycle record of the true acceleration of a real driver-driven host vehicle;
ar3 is the real acceleration of the real driver-driven host vehicle recorded for the third cycle;
……
for the above data, the corresponding data are compared pairwise, that is:
comparing the planned track data with the real track data, namely comparing Ps1 with Pr1, comparing Ps2 with Pr2 and … …;
comparing the planned speed with the real speed, namely comparing Vs1 with Vr1, comparing Vs2 with Vr2, … …;
the planned acceleration is compared with the real acceleration, i.e. As1 is compared with Ar1, As2 is compared with Ar2, … …
Through the above comparison, three sets of comparison results were obtained:
the comparison result of the planned trajectory data and the real trajectory data is { Pc1, Pc2, Pc3 … … Pcn };
the comparison result of the planning speed and the real speed is { Vc1, Vc2, Vc3 … … Vcn };
and comparing the planned acceleration with the real acceleration to obtain { Ac1, Ac2 and Ac3 … … Acn }.
By adopting the comparison result, the difference between the simulation operation data and the real operation data can be calculated, specifically:
and weighting and summing all values in the { Pc1, Pc2 and Pc3 … … Pcn } to obtain an evaluation value aiming at the planned trajectory data, and adopting a Pc evaluation unmanned vehicle algorithm.
And weighting and summing the values in the { Vc1, Vc2 and Vc3 … … Vcn } to obtain an evaluation value aiming at the planning speed data, and adopting a Pc evaluation unmanned vehicle algorithm.
And weighting and summing the values in the { Ac1, Ac2 and Ac3 … … Acn } to obtain an evaluation value aiming at the planned acceleration data, and adopting a Pc evaluation unmanned vehicle algorithm.
Further, the Pc, the Vc and the Ac can be further subjected to weighted summation to finally obtain data C reflecting the integral difference between the simulated operation data and the real operation data, and the unmanned vehicle algorithm can be evaluated by adopting the data C. The smaller the value of C, the closer the unmanned vehicle algorithm is considered to be to a skilled driver level.
In the above embodiment, a weighted sum calculation is used to quantize the difference between two sets of corresponding data. In other embodiments of the present invention, the difference between the two sets of corresponding data may be quantified by other mathematical operations, for example, calculating an average value, calculating a root mean square value, and the like, which is not limited by the present invention.
Therefore, by setting the simulation environment consistent with the real environment of the driver when driving the real host vehicle and comparing the simulation operation data of the unmanned vehicle when running in the simulation environment with the real operation data of the unmanned vehicle when running in the real host vehicle, the obtained comparison result can reflect the difference between the unmanned vehicle algorithm and the real driver driving level, so as to evaluate the advantages and disadvantages of the unmanned vehicle algorithm.
The embodiment of the present invention further provides an evaluation device for an algorithm of an unmanned vehicle, referring to fig. 3, where fig. 3 is a schematic structural diagram of the evaluation device for an algorithm of an unmanned vehicle according to the embodiment of the present invention, and the evaluation device includes:
a simulation environment setting module 310, configured to set a simulation environment so that the simulation environment is consistent with a real environment of a driver when driving a real host;
a simulation operation module 320, configured to operate the unmanned vehicle algorithm in the simulation environment to obtain simulation operation data;
an evaluation module 330, configured to compare the simulated operating data with actual operating data of a driver when driving an actual host vehicle, and evaluate the unmanned vehicle algorithm using a result of the comparison.
Fig. 4 is a schematic structural diagram of an evaluation device for an algorithm of an unmanned vehicle according to an embodiment of the present invention, including:
a simulation environment setting module 310, a simulation execution module 320, and an evaluation module 330. Wherein the content of the first and second substances,
the simulation environment set by the simulation environment setting module 310 may include: a simulation scene and a simulation main vehicle position;
the simulation environment setting module 310 may include:
a simulation scene setting submodule 311 configured to set the simulation scene to be the same as a real scene acquired when the driver drives a real host;
a simulated tow truck position setting sub-module 312 for setting the simulated tow truck position to be the same as the real tow truck position when the driver is driving the real tow truck.
The above-mentioned evaluation module 330 may be configured to:
and comparing at least one item of planned track data, planned speed data and planned acceleration data in the simulation operation data with a corresponding item in real operation data, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
The above-mentioned evaluation module 330 may comprise at least one of the following sub-modules:
the trajectory evaluation sub-module 331 is configured to, for more than one simulated main vehicle position, obtain planned trajectory data of the unmanned vehicle at each simulated main vehicle position and real trajectory data of the real main vehicle at a corresponding real main vehicle position, and compare the planned trajectory data with the real trajectory data; and weighting and summing the comparison results to obtain an evaluation value aiming at planning track data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
A speed evaluation submodule 332, configured to, for more than one simulated host vehicle position, obtain planned speed data of the unmanned vehicle at each simulated host vehicle position and real speed data of a real host vehicle at a corresponding real host vehicle position, and compare the planned speed data with the real speed data; and weighting and summing the comparison results to obtain an evaluation value aiming at planning speed data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
An acceleration evaluation submodule 333 configured to, for more than one simulated host vehicle position, acquire planned acceleration data of the unmanned vehicle at each simulated host vehicle position and real acceleration data of the real host vehicle at a corresponding real host vehicle position, and compare the planned acceleration data with the real acceleration data; and weighting and summing the comparison results to obtain an evaluation value aiming at the planning acceleration data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
The functions of each module in each apparatus in the embodiments of the present invention may refer to the corresponding description in the above method, and are not described herein again.
The embodiment of the present invention further provides an evaluation apparatus for an algorithm of an unmanned vehicle, and as shown in fig. 5, the structural schematic diagram of the evaluation apparatus for an algorithm of an unmanned vehicle according to the embodiment of the present invention includes:
a memory 11 and a processor 12, the memory 11 storing a computer program operable on the processor 12. The processor 12, when executing the computer program, implements the method for obtaining the optimal parameter combination of the recommendation system in the above embodiments. The number of the memory 11 and the processor 12 may be one or more.
The apparatus may further include:
and the communication interface 13 is used for communicating with external equipment and exchanging and transmitting data.
The memory 11 may comprise a high-speed RAM memory, and may further comprise a non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 11, the processor 12 and the communication interface 13 are implemented independently, the memory 11, the processor 12 and the communication interface 13 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 ISA (Extended Industry Standard Architecture), 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. 5, and does not indicate only one bus or one type of bus.
Optionally, in a specific implementation, if the memory 11, the processor 12 and the communication interface 13 are integrated on a chip, the memory 11, the processor 12 and the communication interface 13 may complete communication with each other 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. More specific examples (a non-exhaustive list) of the computer-readable 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 medium could 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.
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 may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes 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.
In summary, the method and the device for evaluating the unmanned vehicle algorithm provided by the embodiment of the invention set the simulation environment consistent with the real environment of the driver when driving the real host, operate the unmanned vehicle in the simulation environment, and then compare the simulation operation data with the real operation data, and the obtained comparison result can reflect the difference between the unmanned vehicle algorithm and the real driver driving level, so as to evaluate the unmanned vehicle algorithm.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (14)
1. A method for evaluating an unmanned vehicle algorithm, the method comprising:
setting a simulation environment to enable the simulation environment to be consistent with a real environment when a driver drives a real host;
running the unmanned vehicle algorithm in the simulation environment to obtain simulation running data;
and comparing the simulated operation data with the real operation data of a driver when driving a real host, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
2. The method of claim 1, wherein the simulation environment comprises: a simulation scene and a simulation main vehicle position;
the setting of the simulation environment comprises:
setting the simulation scene to be the same as a real scene collected by the driver when driving a real host;
setting the simulated host vehicle position to be the same as the real host vehicle position when the driver drives the real host vehicle.
3. The method of claim 1, wherein said comparing said simulated operational data with actual operational data of a driver driving an actual host vehicle, and using the results of said comparing to evaluate said unmanned vehicle algorithm comprises:
and comparing at least one item of planned track data, planned speed data and planned acceleration data in the simulation operation data with a corresponding item in real operation data, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
4. The method of claim 3, wherein comparing planned trajectory data in simulated operational data with corresponding terms in real operational data, and using results of said comparison to evaluate said unmanned vehicle algorithm, comprises:
aiming at more than one simulated main vehicle position, acquiring planned trajectory data of the unmanned vehicle at each simulated main vehicle position and real trajectory data of the real main vehicle at the corresponding real main vehicle position, and comparing the planned trajectory data with the real trajectory data;
and weighting and summing the comparison results to obtain an evaluation value aiming at planning track data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
5. The method of claim 3, wherein comparing planned acceleration data in simulated operational data with corresponding terms in real operational data, and using results of said comparison to evaluate said unmanned vehicle algorithm, comprises:
aiming at more than one simulated main vehicle position, acquiring planned speed data of the unmanned vehicle at each simulated main vehicle position and real speed data of the real main vehicle at the corresponding real main vehicle position, and comparing the planned speed data with the real speed data;
and weighting and summing the comparison results to obtain an evaluation value aiming at planning speed data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
6. The method of claim 3, wherein comparing planned acceleration data in simulated operational data with corresponding terms in real operational data, and using results of said comparison to evaluate said unmanned vehicle algorithm, comprises:
aiming at more than one simulated main vehicle position, acquiring planned acceleration data of the unmanned vehicle at each simulated main vehicle position and real acceleration data of the real main vehicle at the corresponding real main vehicle position, and comparing the planned acceleration data with the real acceleration data;
and weighting and summing the comparison results to obtain an evaluation value aiming at the planning acceleration data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
7. An apparatus for evaluating an algorithm for an unmanned vehicle, the apparatus comprising:
the simulation environment setting module is used for setting a simulation environment to enable the simulation environment to be consistent with a real environment of a driver when the driver drives a real host;
the simulation operation module is used for operating the unmanned vehicle algorithm in the simulation environment to obtain simulation operation data;
and the evaluation module is used for comparing the simulation operation data with the real operation data when a driver drives a real host vehicle, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
8. The apparatus of claim 7, wherein the simulation environment set by the simulation environment setting module comprises: a simulation scene and a simulation main vehicle position;
the simulation environment setting module includes:
the simulation scene setting submodule is used for setting the simulation scene to be the same as a real scene acquired when the driver drives a real host;
and the simulated main vehicle position setting submodule is used for setting the position of the simulated main vehicle to be the same as the real main vehicle position when the driver drives the real main vehicle.
9. The apparatus of claim 7, wherein the evaluation module is configured to:
and comparing at least one item of planned track data, planned speed data and planned acceleration data in the simulation operation data with a corresponding item in real operation data, and evaluating the unmanned vehicle algorithm by adopting the comparison result.
10. The apparatus of claim 9, wherein the evaluation module comprises:
the trajectory evaluation submodule is used for acquiring planning trajectory data of the unmanned vehicle at each simulated main vehicle position and real trajectory data of the real main vehicle at the corresponding real main vehicle position according to more than one simulated main vehicle position, and comparing the planning trajectory data with the real trajectory data; and weighting and summing the comparison results to obtain an evaluation value aiming at planning track data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
11. The apparatus of claim 9, wherein the evaluation module comprises:
the speed evaluation submodule is used for acquiring planning speed data of the unmanned vehicle at each simulated main vehicle position and real speed data of the real main vehicle at the corresponding real main vehicle position according to more than one simulated main vehicle position, and comparing the planning speed data with the real speed data; and weighting and summing the comparison results to obtain an evaluation value aiming at planning speed data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
12. The apparatus of claim 9, wherein the evaluation module comprises:
the acceleration evaluation submodule is used for acquiring planned acceleration data of the unmanned vehicle at each simulated main vehicle position and real acceleration data of the real main vehicle at the corresponding real main vehicle position according to more than one simulated main vehicle position, and comparing the planned acceleration data with the real acceleration data; and weighting and summing the comparison results to obtain an evaluation value aiming at the planning acceleration data, and evaluating the unmanned vehicle algorithm by adopting the evaluation value.
13. An apparatus for evaluating an algorithm for an unmanned vehicle, the apparatus 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 1-6.
14. 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 1-6.
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