CN113353066A - Obstacle touch identification method, device, equipment and storage medium - Google Patents
Obstacle touch identification method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for identifying obstacle touch, wherein the method comprises the following steps: when the target vehicle is in an automatic parking mode, acquiring the speed information of the target vehicle; acquiring acceleration information and wheel rotation speed information of a target vehicle; performing Kalman filtering processing on the vehicle speed information based on the acceleration information and the wheel rotating speed information to obtain target vehicle speed information; acquiring target impact information of a target vehicle; fuzzifying the target vehicle speed information and the target impact degree information to obtain a target vehicle speed fuzzy quantity and a target impact degree fuzzy quantity; carrying out fuzzy reasoning on the target vehicle speed fuzzy quantity and the target impact fuzzy quantity based on a fuzzy controller to obtain a target touch recognition result fuzzy quantity; and performing defuzzification processing on the fuzzy quantity of the target touch identification result to obtain an obstacle touch identification result of the wheels of the target vehicle. The technical scheme who utilizes this application to provide can promote the degree of accuracy of the obstacle touching discernment of wheel.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a method, a device, equipment and a storage medium for identifying obstacle touch.
Background
The existing automatic parking function mainly relies on visual perception for identifying obstacles, image data of the surrounding environment of a vehicle are collected through a visual sensor, and target detection and target positioning are carried out on the image data, so that the obstacles around the vehicle are determined.
However, when the vision sensor is affected by external factors such as dark light in the parking environment, the accuracy of identifying the obstacle is reduced; meanwhile, in the parking process, when the tail of the vehicle approaches to the obstacle, the vision sensor enters the sensing blind area, cannot detect the obstacle in real time, and can increase the excessive torque output for the vehicle to forcibly pass over the obstacle, so that the riding experience of a user is influenced, and even the collision of the vehicle can be caused.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for identifying obstacle touch, so that the accuracy of the obstacle touch identification of wheels can be improved, vehicles are prevented from forcibly crossing obstacles, riding experience of users is improved, and the technical scheme of the application is as follows:
in one aspect, an obstacle touch identification method is provided, and the method includes:
when a target vehicle is in an automatic parking mode, acquiring the speed information of the target vehicle;
acquiring acceleration information and wheel rotation speed information of the target vehicle;
performing Kalman filtering processing on the vehicle speed information based on the acceleration information and the wheel rotating speed information to obtain target vehicle speed information;
acquiring target impact information of the target vehicle;
fuzzifying the target vehicle speed information and the target impact degree information to obtain a target vehicle speed fuzzy quantity and a target impact degree fuzzy quantity;
performing fuzzy reasoning on the target vehicle speed fuzzy quantity and the target impact fuzzy quantity based on a fuzzy controller to obtain a target touch recognition result fuzzy quantity;
and performing defuzzification processing on the fuzzy quantity of the target touch identification result to obtain an obstacle touch identification result of the wheels of the target vehicle.
In another aspect, an obstacle touch recognition apparatus is provided, the apparatus including:
the system comprises a vehicle speed information acquisition module, a vehicle speed information acquisition module and a vehicle speed information acquisition module, wherein the vehicle speed information acquisition module is used for acquiring the vehicle speed information of a target vehicle when the target vehicle is in an automatic parking mode;
the information acquisition module is used for acquiring acceleration information and wheel rotating speed information of the target vehicle;
the Kalman filtering processing module is used for carrying out Kalman filtering processing on the vehicle speed information based on the acceleration information and the wheel rotating speed information to obtain target vehicle speed information;
the target impact information acquisition module is used for acquiring target impact information of the target vehicle;
the fuzzification processing module is used for fuzzifying the target vehicle speed information and the target impact degree information to obtain a target vehicle speed fuzzy quantity and a target impact degree fuzzy quantity;
the fuzzy inference module is used for carrying out fuzzy inference on the target vehicle speed fuzzy quantity and the target impact degree fuzzy quantity based on a fuzzy controller to obtain a target touch recognition result fuzzy quantity;
and the defuzzification processing module is used for performing defuzzification processing on the fuzzy quantity of the target touch identification result to obtain an obstacle touch identification result of the wheels of the target vehicle.
In another aspect, an obstacle touch recognition device is provided, which includes a processor and a memory, where at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the obstacle touch recognition method as described above.
In another aspect, a computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the obstacle touch identification method as described above.
The obstacle touch identification method, the obstacle touch identification device, the obstacle touch identification equipment and the storage medium have the following technical effects:
utilize the technical scheme that this application provided, through carrying out kalman filtering to speed of a motor vehicle information, obtain target speed of a motor vehicle information to carry out obstacle touching discernment based on target speed of a motor vehicle information and target impulse degree information, when the wheel of discerning the target vehicle touches the obstacle, control target vehicle stops, thereby can promote the degree of accuracy of the obstacle touching discernment of wheel and avoid the vehicle to export too much moment of torsion because of crossing the obstacle by force, produce the accident, and promote user's experience of riding.
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In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart illustrating an obstacle touch identification method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a kalman filtering processing method according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a fuzzy rule generating method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a fuzzification processing method provided in an embodiment of the present application;
FIG. 5 is a schematic diagram of a first membership function provided in an embodiment of the present application;
FIG. 6 is a diagram of a second membership function provided in an embodiment of the present application;
fig. 7 is a schematic flow chart of a fuzzy inference method according to an embodiment of the present application;
FIG. 8 is a diagram of a third membership function provided in an embodiment of the present application;
fig. 9 is a schematic view of an obstacle touch recognition device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or 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 server 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.
An obstacle touch recognition method provided in the present embodiment is introduced below, and fig. 1 is a schematic flow chart of the obstacle touch recognition method provided in the present embodiment. It is noted that the present specification provides the method steps as described in the examples or flowcharts, but may include more or less steps based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In actual system or product execution, sequential execution or parallel execution (e.g., parallel processor or multi-threaded environment) may be possible according to the embodiments or methods shown in the figures. Specifically, as shown in fig. 1, the method may include:
s101, when the target vehicle is in an automatic parking mode, acquiring the speed information of the target vehicle.
In an embodiment of the present disclosure, the target vehicle may be a vehicle that may have an automatic parking function, and the vehicle speed information may be acquired by a vehicle speed sensor of the target vehicle.
And S103, acquiring the acceleration information and the wheel rotating speed information of the target vehicle.
In practical applications, the wheel speed information may be collected by a wheel speed sensor mounted on a wheel of the target vehicle, and the acceleration information may be collected by an acceleration sensor of the target vehicle.
And S105, performing Kalman filtering processing on the vehicle speed information based on the acceleration information and the wheel rotating speed information to obtain target vehicle speed information.
In a specific embodiment, as shown in fig. 2, the performing kalman filtering on the vehicle speed information based on the acceleration information and the wheel rotation speed information to obtain the target vehicle speed information may include:
and S201, determining a state vector of the Kalman filter according to the vehicle speed information and the acceleration information.
Specifically, the vehicle speed information may be represented by v (k), the acceleration information may be represented by a (k), and the state vector of the kalman filter may be represented by x (k) ([ v (k), a (k))]T。
And S203, determining the observation vector of the Kalman filter according to the wheel rotating speed information and preset wheel radius information.
In practical application, the wheel rotation speed information and the wheel radius information can be multiplied to obtain the linear velocity information of the wheel, and the vehicle speed information can be predicted and corrected through the linear velocity information.
Specifically, the vehicle speed information may be represented by ω (k), the wheel radius information may be represented by r, and the observation vector of the kalman filter may be represented by y (k) ([ r ω (k) ].
And S205, respectively constructing a state equation and an observation equation of the Kalman filter based on the state vector and the observation vector.
Specifically, the construction of the state equation is as follows: x (k) ═ AX (k-1) + W (k-1);
the observation equation is constructed as follows: y (k) ═ HX (k-1) + V (k-1);
wherein A is a state transition matrix,
t represents time, H is an observation matrix, and H is [1,0 ]]TW (k) is system noise, V (k) is observation noise, and generally, V (k) and V (k) are white noises which are independent and normally distributed, W-N (0, Q), Q is a covariance matrix of the system noise, and V to (0, R), R is a covariance matrix of the observation noise.
And S207, performing iterative optimization based on the state equation and the observation equation to obtain the target vehicle speed information.
Specifically, iterative optimization may be performed based on a time update formula and a measurement state update formula to obtain optimally estimated vehicle speed information, and the optimally estimated vehicle speed information is used as target vehicle speed information. Wherein, the time updating formula may include: the prior state prediction formula and the prior state covariance formula, and the measurement state update formula may include: the kalman gain formula, the posterior state prediction formula, and the posterior state covariance formula are specifically as follows:
a priori state prediction formula:
prior state covariance formula:
kalman gain formula:
the posterior state prediction formula:
the posterior state covariance formula:
according to the technical scheme provided by the embodiment, the vehicle speed information can be subjected to Kalman filtering processing based on the acceleration information and the wheel rotating speed information, so that the vehicle speed information with higher accuracy is obtained, and the accuracy of fuzzy identification of the obstacle is improved.
And S107, acquiring the target impact degree information of the target vehicle.
In practical application, the impact degree information can be the force transmitted to the vehicle body by the impact force received by the wheels of the vehicle, and the smoothness of vehicle driving can be reflected by the size of the impact degree information.
Specifically, the target impact information may be obtained by performing a second-order derivative calculation on the target vehicle speed information.
And S109, performing fuzzification processing on the target vehicle speed information and the target impact degree information to obtain a target vehicle speed fuzzy amount and a target impact degree fuzzy amount.
Specifically, the target vehicle speed fuzzy amount may include: the target vehicle speed membership grade and the membership degree corresponding to the target vehicle speed membership grade; the target impact blur amount may include: and the target impact degree membership grade and the membership degree corresponding to the target impact degree membership grade.
In this embodiment, based on fuzzy recognition performed on target vehicle speed information and target impact information by a fuzzy controller, a result of identifying obstacle touching of wheels of a target vehicle may be obtained, in practical applications, the fuzzy controller may include, but is not limited to, a fuzzification interface, a fuzzy rule base, a fuzzy inference engine, and a defuzzification interface, where the fuzzy rule base is composed of a plurality of fuzzy rules, as shown in fig. 3, fig. 3 is a schematic flow diagram of a fuzzy rule generation method provided in this embodiment, and specifically, the method may include:
s301, a preset vehicle speed fuzzy subset, a preset impact degree fuzzy subset and a preset touch identification result fuzzy subset are obtained, wherein the vehicle speed fuzzy subset comprises a plurality of vehicle speed membership grades, the impact degree fuzzy subset comprises a plurality of impact degree membership grades, and the touch identification result fuzzy subset comprises a plurality of touch identification result membership grades.
Specifically, the vehicle speed fuzzy subset may be formed by a plurality of vehicle speed membership grades, and the classification of the vehicle speed membership grades may include, but is not limited to: low speed, lower speed, medium speed, higher speed, high speed. The fuzzy subset of the degree of impact may be composed of a plurality of degrees of impact membership grades, and the division of the degrees of impact membership grades may include, but is not limited to: small, medium, large. The fuzzy subset of touch recognition results may be formed by multiple touch recognition result membership grades, and the classification of the touch recognition result membership grades may include but is not limited to: yes, no.
And S303, determining the fuzzy relation between the input variable and the output variable by taking the plurality of vehicle speed membership grades and the plurality of impact membership grades as the input variable of the fuzzy controller and taking the plurality of touch identification result membership grades as the output variable of the fuzzy controller.
In a specific embodiment, the vehicle speed membership levels in the fuzzy subset of vehicle speeds include low (S), medium (M) and high (B), and the jerk membership levels in the fuzzy subset of jerks include: small (PS), medium (PM) and large (PB), the touch recognition result membership grade in the fuzzy subset of touch recognition results includes yes (Y) and no (N), and 9 fuzzy relations are determined:
if V (target vehicle speed fuzzy quantity) is S and J (target impact fuzzy quantity) is S Then F (target touch recognition result fuzzy quantity) is N;
If V＝S and J＝PS Then F＝N；
If V＝S and J＝PB Then F＝Y；
If V＝M and J＝PS Then F＝N；
If V＝M and J＝PM Then F＝N；
If V＝M and J＝PB Then F＝Y；
If V＝B and J＝PS Then F＝N；
If V＝B and J＝PM Then F＝Y；
If V＝B and J＝PB Then F＝Y。
s305 generates the fuzzy rule based on the fuzzy relation.
Specifically, the fuzzy rule in the above specific embodiment is as follows:
| F | PS(J) | PM(J) | PB(J) |
| S(V) | N | N | Y |
| M(V) | N | N | Y |
| B(V) | N | Y | Y |
in the embodiment of the present disclosure, as shown in fig. 4, the blurring the target vehicle speed information and the target jerk information to obtain the target vehicle speed blur amount and the target jerk blur amount may include:
s401, fuzzifying the target vehicle speed information based on a preset first membership function to obtain the target vehicle speed membership grade and the membership degree corresponding to the target vehicle speed membership grade.
Specifically, the preset first membership function is used to characterize membership and membership degree between elements in the vehicle speed theory domain and vehicle speed membership levels in the vehicle speed fuzzy subset, and the first membership function may include, but is not limited to, a gaussian membership function. The above mentioned speed domain may be [0, a ] km/h, b may be set based on the highest speed limit in the automatic parking function of the target vehicle in practical use, for example, a may take a positive number not greater than 5.
Taking the speed domain of discourse as [0,5] km/h and the classification of the speed membership grade into low speed, medium speed and fast speed as an example, as shown in fig. 5, fig. 5 is a schematic diagram of a first membership function provided in the embodiment of the present application.
And S403, fuzzifying the target impact degree information based on a preset second membership function to obtain the target impact degree membership grade and a membership degree corresponding to the target impact degree membership grade.
Specifically, the preset second membership function is used to characterize membership and membership between elements in the impulse theory domain and impulse membership levels in the impulse fuzzy subset, and the second membership function may include, but is not limited to, a generalized bell-type membership function. The domain of impact may be [0, b ]]m/s3B may be set based on the maximum impact that may be collected when the target vehicle performs automatic parking in practical applications, and optionally, b may be greater than 80.
The domain of impact is [0,100 ]]m/s3For example, the impact membership grade is divided into small, medium and large, as shown in fig. 6, and fig. 6 is a schematic diagram of a second membership function provided in the embodiment of the present application.
And S111, carrying out fuzzy reasoning on the target vehicle speed fuzzy quantity and the target impact fuzzy quantity based on a fuzzy controller to obtain a target touch recognition result fuzzy quantity.
In this embodiment, as shown in fig. 7, the fuzzy inference of the target vehicle speed fuzzy quantity and the target impact fuzzy quantity based on the fuzzy controller to obtain the target touch recognition result fuzzy quantity may include:
and S701, determining a target touch identification result membership grade corresponding to the target vehicle speed fuzzy quantity and the target impact degree fuzzy quantity and a membership degree corresponding to the target touch identification result membership grade based on a fuzzy rule in the fuzzy controller.
And S703, taking the target touch recognition result membership grade and the membership degree corresponding to the target touch recognition result membership grade as the target touch recognition result fuzzy quantity.
In a specific embodiment, if the target vehicle speed information is 1.25km/h, the target impact information is 26m/s3From fig. 5 and 6, S and M times 0.5 times the target vehicle speed blur amount (V), PM times 0.1 times the target impact blur amount (J), and PB times 0.3 times the target vehicle speed blur amount (V) can be obtained.
When determining the fuzzy amount of the target touch identification result, adopting a Max-Min fuzzy reasoning method;
if V0.5 times S and J0.1 times PM Then F0.1 times N;
if V0.5 times M and J0.1 times PM Then F0.1 times N;
if V0.5 times S and J0.3 times PB Then F0.3 times Y;
if V0.5 times M and J0.3 times PB Then F0.3 times Y;
and obtaining N of which the target touch recognition result fuzzy amount is 0.1 time, N of which is 0.1 time, Y of which is 0.3 time and Y of which is 0.3 time.
And S113, performing defuzzification processing on the fuzzy amount of the target touch recognition result to obtain an obstacle touch recognition result of the wheels of the target vehicle.
Specifically, the central values corresponding to the membership grades of different target touch recognition results may be determined based on a preset third membership function. The preset third membership function is used to represent a membership relationship and a membership degree between elements in the touch recognition result theory domain and the touch recognition result membership grade in the touch recognition result fuzzy subset, and the third membership function may include, but is not limited to, a trapezoidal membership function.
Taking a domain of the touch recognition result as [0,1], whether the membership grade of the touch recognition result is divided into yes and no, as shown in fig. 8, fig. 8 is a schematic diagram of a third membership function provided in the embodiment of the present application, and specifically, the formula of the third membership function is:
in this embodiment, the target touch recognition result membership grade and the membership degree corresponding to the target touch recognition result membership grade may be defuzzified based on a gravity center method to obtain the obstacle touch recognition result.
Specifically, when the target touch recognition result is subordinate to the rank (Y),
based on the formula of the center of gravity
The center of gravity value corresponding to (Y) is obtained to be 0.674;
when the target touch identification result is in the negative (N) membership grade,
based on the formula of the center of gravity
If not, the corresponding gravity center value of (N) is 0.176;
and performing weighted calculation on four values in the target touch recognition result fuzzy quantity to obtain:
in an optional embodiment, when the obstacle touch recognition result is that the wheels of the target vehicle touch an obstacle, the target vehicle is controlled to stop.
Specifically, taking the touch recognition result domain as [0,1] as an example, the threshold value of the obstacle touch recognition result between the wheel touching the obstacle and the wheel not touching the obstacle may be determined based on a calibration method, in an optional embodiment, the threshold value of the obstacle touch recognition result may be 0.4, when the value of the obstacle touch recognition result is less than 0.4, the wheel does not touch the obstacle, and when the value of the obstacle touch recognition result is greater than or equal to 0.4, the wheel touches the obstacle. For example, since the obstacle touch recognition result has a numerical value of 0.5495, it is considered that the wheels of the target vehicle touch the obstacle, and the target vehicle is controlled to stop.
According to the technical scheme provided by the embodiment of the application, the target speed information is obtained by performing Kalman filtering processing on the speed information, the obstacle touch identification is performed on the basis of the target speed information and the target impact degree information, when the obstacle is touched by the wheel of the target vehicle, the target vehicle is controlled to brake, the accuracy of the obstacle touch identification of the wheel can be improved, and therefore accidents caused by the fact that the vehicle forcibly crosses the obstacle are avoided, and the riding experience of a user is improved.
An embodiment of the present application provides an obstacle touch recognition device, as shown in fig. 9, the device may include:
a vehicle speed information collecting module 910, configured to collect vehicle speed information of a target vehicle when the target vehicle is in an automatic parking mode;
an information obtaining module 920, configured to obtain acceleration information and wheel rotation speed information of the target vehicle;
a kalman filtering module 930, configured to perform kalman filtering on the vehicle speed information based on the acceleration information and the wheel rotation speed information to obtain target vehicle speed information;
a target impact information acquiring module 940 for acquiring target impact information of the target vehicle;
a fuzzification processing module 950, configured to perform fuzzification processing on the target vehicle speed information and the target impact information to obtain a target vehicle speed fuzzy amount and a target impact fuzzy amount;
the fuzzy inference module 960 is used for carrying out fuzzy inference on the target vehicle speed fuzzy quantity and the target impact fuzzy quantity based on a fuzzy controller to obtain a target touch recognition result fuzzy quantity;
a defuzzification processing module 970, configured to perform defuzzification processing on the target touch recognition result fuzzy amount to obtain an obstacle touch recognition result of the wheel of the target vehicle.
In a specific embodiment, the kalman filter processing module 930 may include:
the state vector unit is used for determining a state vector of the Kalman filter according to the vehicle speed information and the acceleration information;
the observation vector unit is used for determining the observation vector of the Kalman filter according to the wheel rotating speed information and preset wheel radius information;
an equation constructing unit, configured to respectively construct a state equation and an observation equation of the kalman filter based on the state vector and the observation vector;
and the iterative optimization unit is used for performing iterative optimization based on the state equation and the observation equation to obtain the target vehicle speed information.
In an embodiment of the present specification, the apparatus may further include:
the system comprises a fuzzy subset acquisition unit, a fuzzy subset acquisition unit and a fuzzy recognition result processing unit, wherein the fuzzy subset acquisition unit is used for acquiring a preset vehicle speed fuzzy subset, a preset impact degree fuzzy subset and a preset touch recognition result fuzzy subset, the vehicle speed fuzzy subset comprises a plurality of vehicle speed membership grades, the impact degree fuzzy subset comprises a plurality of impact degree membership grades, and the touch recognition result fuzzy subset comprises a plurality of touch recognition result membership grades;
a fuzzy relation determining unit, configured to determine a fuzzy relation between the input variable and the output variable by using the plurality of vehicle speed membership levels and the plurality of impact membership levels as input variables of the fuzzy controller and using the plurality of touch recognition result membership levels as output variables of the fuzzy controller;
and a fuzzy rule generating unit for generating the fuzzy rule based on the fuzzy relation.
In this embodiment, the fuzzification processing module 950 may include:
the target vehicle speed information processing unit is used for fuzzifying the target vehicle speed information based on a preset first membership function to obtain a target vehicle speed membership grade and a membership degree corresponding to the target vehicle speed membership grade;
and the target impact degree information processing unit is used for fuzzifying the target impact degree information based on a preset second membership function to obtain the target impact degree membership grade and the membership degree corresponding to the target impact degree membership grade.
In this embodiment, the fuzzy inference module 960 may include:
a target touch identification result membership grade determining unit, configured to determine a target touch identification result membership grade corresponding to the target vehicle speed fuzzy quantity and the target impact degree fuzzy quantity and a membership grade corresponding to the target touch identification result membership grade based on a fuzzy rule in the fuzzy controller;
and the target touch identification result fuzzy quantity unit is used for taking the target touch identification result membership grade and the membership degree corresponding to the target touch identification result membership grade as the target touch identification result fuzzy quantity.
The device and method embodiments in the device embodiment described above are based on the same inventive concept.
The embodiment of the application provides an obstacle touch recognition device, which comprises a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded and executed by the processor to realize the obstacle touch recognition method provided by the above method embodiment.
The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to the use of the above-described apparatus, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.
The method provided by the embodiment of the application can be executed in a vehicle-mounted terminal or a similar operation device, that is, the computer device can comprise the vehicle-mounted terminal or the similar operation device.
The present application further provides a storage medium, where the storage medium may be disposed in a server to store at least one instruction or at least one program for implementing the method for identifying obstacle touching according to one of the method embodiments, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method for identifying obstacle touching according to the method embodiment.
Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
According to the embodiment of the obstacle touch identification method, the device, the equipment or the storage medium, the target speed information is obtained by performing Kalman filtering processing on the speed information, the obstacle touch identification is performed based on the target speed information and the target impact degree information, when the obstacle is touched by the wheel of the target vehicle, the target vehicle is controlled to stop, the accuracy of the obstacle touch identification of the wheel can be improved, the situation that the vehicle outputs too much torque due to the fact that the vehicle forcibly crosses the obstacle, accidents are caused is avoided, and the riding experience of a user is improved.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device and storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program to instruct relevant hardware to implement the above program, and the above program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (11)
1. An obstacle touch recognition method, the method comprising:
when a target vehicle is in an automatic parking mode, acquiring the speed information of the target vehicle;
acquiring acceleration information and wheel rotation speed information of the target vehicle;
performing Kalman filtering processing on the vehicle speed information based on the acceleration information and the wheel rotating speed information to obtain target vehicle speed information;
acquiring target impact information of the target vehicle;
fuzzifying the target vehicle speed information and the target impact degree information to obtain a target vehicle speed fuzzy quantity and a target impact degree fuzzy quantity;
performing fuzzy reasoning on the target vehicle speed fuzzy quantity and the target impact fuzzy quantity based on a fuzzy controller to obtain a target touch recognition result fuzzy quantity;
and performing defuzzification processing on the fuzzy quantity of the target touch identification result to obtain an obstacle touch identification result of the wheels of the target vehicle.
2. The method according to claim 1, wherein the target vehicle speed fuzzy quantity comprises a target vehicle speed membership grade and a membership grade corresponding to the target vehicle speed membership grade, the target impact degree fuzzy quantity comprises a target impact degree membership grade and a membership grade corresponding to the target impact degree membership grade, and the fuzzifying processing of the target vehicle speed information and the target impact degree information to obtain the target vehicle speed fuzzy quantity and the target impact degree fuzzy quantity comprises:
fuzzifying the target vehicle speed information based on a preset first membership function to obtain a target vehicle speed membership grade and a membership degree corresponding to the target vehicle speed membership grade;
fuzzification processing is carried out on the target impact degree information based on a preset second membership function, and the target impact degree membership grade and the membership degree corresponding to the target impact degree membership grade are obtained.
3. The method according to claim 1, wherein the fuzzy controller-based fuzzy inference on the target vehicle speed fuzzy quantity and the target impact fuzzy quantity to obtain a target touch recognition result fuzzy quantity comprises:
determining a target touch identification result membership grade corresponding to the target vehicle speed fuzzy quantity and the target impact degree fuzzy quantity and a membership degree corresponding to the target touch identification result membership grade based on a fuzzy rule in the fuzzy controller;
and taking the target touch identification result membership grade and the membership degree corresponding to the target touch identification result membership grade as the target touch identification result fuzzy quantity.
4. The method of claim 3, further comprising:
the method comprises the steps of obtaining a preset vehicle speed fuzzy subset, a preset impact degree fuzzy subset and a preset touch identification result fuzzy subset, wherein the vehicle speed fuzzy subset comprises a plurality of vehicle speed membership grades, the impact degree fuzzy subset comprises a plurality of impact degree membership grades, and the touch identification result fuzzy subset comprises a plurality of touch identification result membership grades;
determining a fuzzy relation between the input variable and the output variable by taking the plurality of vehicle speed membership grades and the plurality of impact membership grades as input variables of the fuzzy controller and taking the plurality of touch identification result membership grades as output variables of the fuzzy controller;
and generating the fuzzy rule based on the fuzzy relation.
5. The method according to claim 3, wherein the defuzzifying the target touch recognition result fuzzy amount to obtain the obstacle touch recognition result of the wheel of the target vehicle comprises:
and performing defuzzification processing on the target touch identification result membership grade and the membership degree corresponding to the target touch identification result membership grade based on a gravity center method to obtain the obstacle touch identification result.
6. The method according to any one of claims 1 to 5, wherein the performing Kalman filtering on the vehicle speed information based on the acceleration information and the wheel rotation speed information to obtain target vehicle speed information comprises:
determining a state vector of a Kalman filter according to the vehicle speed information and the acceleration information;
determining an observation vector of the Kalman filter according to the wheel rotating speed information and preset wheel radius information;
respectively constructing a state equation and an observation equation of the Kalman filter based on the state vector and the observation vector;
and carrying out iterative optimization based on the state equation and the observation equation to obtain the target vehicle speed information.
7. The method of any one of claims 1 to 5, wherein the obtaining target impact information for the target vehicle comprises:
and performing second-order derivative calculation on the target vehicle speed information to obtain the target impact information.
8. The method according to any one of claims 1 to 5, wherein after the defuzzifying the target touch recognition result fuzzy amount to obtain the obstacle touch recognition result of the wheel of the target vehicle, the method further comprises:
and when the touch identification result is that the wheels of the target vehicle touch the obstacle, controlling the target vehicle to brake.
9. An obstacle touch recognition device, characterized in that the device comprises:
the system comprises a vehicle speed information acquisition module, a vehicle speed information acquisition module and a vehicle speed information acquisition module, wherein the vehicle speed information acquisition module is used for acquiring the vehicle speed information of a target vehicle when the target vehicle is in an automatic parking mode;
the information acquisition module is used for acquiring acceleration information and wheel rotating speed information of the target vehicle;
the Kalman filtering processing module is used for carrying out Kalman filtering processing on the vehicle speed information based on the acceleration information and the wheel rotating speed information to obtain target vehicle speed information;
the target impact information acquisition module is used for acquiring target impact information of the target vehicle;
the fuzzification processing module is used for fuzzifying the target vehicle speed information and the target impact degree information to obtain a target vehicle speed fuzzy quantity and a target impact degree fuzzy quantity;
the fuzzy inference module is used for carrying out fuzzy inference on the target vehicle speed fuzzy quantity and the target impact degree fuzzy quantity based on a fuzzy controller to obtain a target touch recognition result fuzzy quantity;
and the defuzzification processing module is used for performing defuzzification processing on the fuzzy quantity of the target touch identification result to obtain an obstacle touch identification result of the wheels of the target vehicle.
10. An obstacle touch recognition device, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the obstacle touch recognition method according to any one of claims 1 to 8.
11. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the method according to any one of claims 1 to 8.
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