CN115257736A

CN115257736A - Vehicle distance keeping speed planning method based on fuzzy inference truth value evolution

Info

Publication number: CN115257736A
Application number: CN202211001373.1A
Authority: CN
Inventors: 孟天闯; 黄晋; 李惠乾; 李星宇; 杨殿阁
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2022-08-19
Filing date: 2022-08-19
Publication date: 2022-11-01
Anticipated expiration: 2042-08-19
Also published as: CN115257736B

Abstract

The application relates to a vehicle distance keeping speed planning method, device, computer equipment, storage medium and computer program product based on fuzzy inference true value evolution. The method comprises the following steps: and acquiring a current vehicle distance error between the current vehicle distance and the expected vehicle distance and a current vehicle speed error between the current vehicle speed and the expected vehicle speed. And determining the vehicle distance error membership degree corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error, and determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error. And determining the acceleration which enables the value of the inference loss function to be in the minimum value according to the vehicle distance error membership, the vehicle speed error membership, the membership empirical function, the membership function corresponding to the acceleration and the inference loss function, and taking the acceleration as the expected acceleration. By the method, the accuracy of the expected acceleration can be improved.

Description

Vehicle distance keeping speed planning method based on fuzzy inference truth value evolution

Technical Field

The application relates to the technical field of automatic driving, in particular to a vehicle distance keeping speed planning method based on fuzzy inference truth value evolution.

Background

With the development of the field of automatic driving technology, automatic following technology has emerged, specifically, by adjusting the speed of the own vehicle to maintain a fixed relative distance from the preceding vehicle.

In the related art, an empirical travel speed table is generally established by using historical travel data of a vehicle, that is, when a vehicle speed of a preceding vehicle, a vehicle distance between the preceding vehicle and the vehicle speed of the vehicle are within a certain value range, the vehicle acceleration should be adjusted to a certain expected acceleration. Among them, the difference of the empirical travel speed tables established by different persons is large, and therefore, the accuracy in manually establishing the empirical travel speed table is low.

Because the accuracy of the empirical driving speed table is directly determined, the accuracy of the expected acceleration determined in the related art is also low, and the accuracy of vehicle speed planning for the vehicle based on the accuracy is also low.

Disclosure of Invention

Accordingly, there is a need to provide a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for vehicle distance keeping speed planning based on fuzzy inference truth value evolution, which can improve the accuracy of vehicle speed planning.

In a first aspect, the present application provides a vehicle speed planning method. The method comprises the following steps:

acquiring a current vehicle distance error between a current vehicle distance and an expected vehicle distance and a current vehicle speed error between a current vehicle speed and an expected vehicle speed;

determining the vehicle distance error membership degree corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error; determining a vehicle speed error membership degree corresponding to the current vehicle speed error according to a membership function corresponding to the vehicle speed error;

determining the acceleration which enables the value of the inference loss function to be in the minimum value according to the vehicle distance error membership degree, the vehicle speed error membership degree, the membership degree empirical function, the membership degree function corresponding to the acceleration and the inference loss function, and taking the acceleration as the expected acceleration of the vehicle; the membership degree empirical function is used for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error; and the inference loss function is used for reflecting the loss degree of the inference credibility of the expected acceleration to the vehicle speed error and the vehicle distance error.

In one embodiment, before determining the acceleration at which the value of the inference loss function is at the minimum according to the vehicle distance error membership, the vehicle speed error membership, the membership empirical function, the membership function corresponding to the acceleration, and the inference loss function, as the expected acceleration, the method further includes:

acquiring historical driving experience data of the vehicle; the historical experience data comprises acceleration, vehicle distance error and vehicle speed error;

respectively determining the acceleration, the vehicle distance error, the historical acceleration membership of the vehicle speed error, the historical vehicle distance error membership and the historical vehicle speed error membership in the historical driving experience data according to the membership function corresponding to the vehicle distance error, the membership function corresponding to the vehicle speed error and the membership function corresponding to the acceleration;

and fitting to obtain a membership degree empirical function for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error according to the historical acceleration membership degree, the historical vehicle distance error membership degree and the historical vehicle speed error membership degree.

In one embodiment, the vehicle distance error membership degree corresponding to the current vehicle distance error is determined according to the membership function corresponding to the vehicle distance error; before determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error, the method further comprises the following steps:

respectively acquiring boundary parameters of expected acceleration, vehicle speed error and vehicle distance error;

constructing a function of which the boundary parameter accords with the boundary parameter corresponding to the vehicle speed error, and taking the function as a membership function of the vehicle speed error;

constructing a function of which the boundary parameter accords with the boundary parameter corresponding to the vehicle distance error, and taking the function as a membership function of the vehicle distance error;

and constructing a function of which the boundary parameter accords with the boundary parameter corresponding to the expected acceleration as a membership function corresponding to the expected acceleration.

In one of the embodiments, the first and second electrodes are, the acquiring of the boundary parameter of the expected acceleration comprises the following steps:

acquiring vehicle data and environment data;

and determining an upper limit acceleration value and a lower limit acceleration value corresponding to the expected acceleration according to the vehicle data and the environment data, and taking the upper limit acceleration value and the lower limit acceleration value as boundary parameters of the expected acceleration.

In one embodiment, the obtaining boundary parameters of the vehicle speed error and the vehicle distance error includes:

acquiring sensitivity setting parameters;

determining boundary parameters of the vehicle speed error and the vehicle distance error according to the sensitivity setting parameters; the sensitivity setting parameter is inversely proportional to the range of the boundary parameter of the vehicle speed error and the vehicle distance error.

In one embodiment, the obtaining a current vehicle distance error between a current vehicle distance and an expected vehicle distance and a current vehicle speed error between a current vehicle speed and an expected vehicle speed includes:

acquiring the current speed of the vehicle, the current distance between the vehicle and a preceding vehicle, the current speed of the preceding vehicle and the expected distance between the vehicle and the preceding vehicle;

and determining the difference between the current vehicle speed of the vehicle and the current vehicle speed of the preceding vehicle as a current vehicle speed error, and determining the difference between the current vehicle distance and the expected vehicle distance as a current vehicle distance error.

In a second aspect, the application further provides a vehicle distance keeping speed planning device based on fuzzy reasoning true value evolution. The device comprises:

the acquisition module is used for acquiring a current vehicle distance error between a current vehicle distance and an expected vehicle distance and a current vehicle speed error between a current vehicle speed and an expected vehicle speed;

the membership degree determining module is used for determining the membership degree of the vehicle distance error corresponding to the current vehicle distance error according to the membership degree function corresponding to the vehicle distance error; determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error;

the acceleration determining module is used for determining the acceleration which enables the value of the inference loss function to be in the minimum value according to the vehicle distance error membership degree, the vehicle speed error membership degree, the membership degree empirical function, the membership degree function corresponding to the acceleration and the inference loss function, and the acceleration is used as the expected acceleration of the vehicle; the membership degree empirical function is used for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error; and the inference loss function is used for reflecting the loss degree of the inference reliability of the expected acceleration to the vehicle speed error and the vehicle distance error.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:

determining the acceleration which enables the value of the inference loss function to be in the minimum value according to the vehicle distance error membership degree, the vehicle speed error membership degree, the membership degree empirical function, the membership degree function corresponding to the acceleration and the inference loss function, and taking the acceleration as the expected acceleration of the vehicle; the membership degree empirical function is used for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error; and the inference loss function is used for reflecting the loss degree of the inference reliability of the expected acceleration to the vehicle speed error and the vehicle distance error.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:

determining the membership degree of the vehicle distance error corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error; determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error;

determining the acceleration with the value of the inference loss function at the minimum value according to the vehicle distance error membership degree, the vehicle speed error membership degree, the membership degree empirical function, the membership degree function corresponding to the acceleration and the inference loss function, and taking the acceleration as the expected acceleration of the vehicle; the membership degree empirical function is used for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error; and the inference loss function is used for reflecting the loss degree of the inference credibility of the expected acceleration to the vehicle speed error and the vehicle distance error.

According to the vehicle distance keeping speed planning method based on the fuzzy reasoning true value evolution, the device, the computer equipment, the storage medium and the computer program product, the expected acceleration is obtained through reasoning in a mode of conforming to the fuzzy reasoning true value non-increasing mode, the accuracy of the determined expected acceleration is higher compared with the mode in the related technology, the expected acceleration of a vehicle is planned according to the vehicle distance keeping speed planning method based on the fuzzy reasoning true value evolution, and the accuracy of vehicle distance keeping is also higher.

Drawings

FIG. 1 is a schematic flow chart illustrating a method for vehicle distance keeping speed planning based on fuzzy inference truth evolution according to an embodiment;

FIG. 2 is a block diagram of a vehicle distance maintaining speed planning apparatus based on fuzzy inference truth evolution in an embodiment;

FIG. 3 is an internal block diagram of a computer device integrated with a vehicle interior in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In the related art, an empirical travel speed table is generally configured, and the empirical travel speed table stores a plurality of conditions and corresponding expected accelerations, for example, the empirical travel speed table stores a plurality of vehicle distance states and a plurality of vehicle speed states, any one safe distance value, a time value of two-vehicle collision, and a probability value of each vehicle distance state of the corresponding host vehicle and a probability value of each vehicle speed state of the host vehicle in the case of the expected vehicle speed value of the host vehicle. The empirical running speed table also stores a plurality of vehicle working condition states, preset acceleration values corresponding to the vehicle working condition states, vehicle distance states corresponding to the vehicle working condition states and vehicle speed states.

The equipment calculates the probability value of each vehicle working condition state of the vehicle according to the probability value of each vehicle working condition state of the vehicle and the probability value of each vehicle speed state of the vehicle through a preset vehicle working condition state probability algorithm, then determines the probability value of each vehicle working condition state of the vehicle and the probability value of each vehicle speed state of the vehicle according to a safety distance value, a time value of collision between the two vehicles and an expected vehicle speed value of the vehicle by referring to an empirical driving speed table, and then obtains the expected acceleration value of the vehicle according to the probability value of each vehicle working condition state and the preset acceleration value of each vehicle working condition state.

Specifically, the following four conditions: 1. the distance between the front vehicles is less than a preset distance value, and the preset distance value is a certain determined value between 5 and 20 meters. 2. The collision time value of the two vehicles of the vehicle and the front vehicle is less than a preset time value, and the preset time value is a certain determined value between 3 and 10 seconds. 3. The ratio of the front vehicle distance to the safe distance value ds is smaller than a preset threshold value, and the preset threshold value is a certain determined value between 30% and 45%. 4. The expected vehicle speed value of the vehicle is less than the vehicle speed of the vehicle, the difference is greater than or equal to a preset first difference, and the preset first difference is a certain determined value between 15 and 25 km/h. If the vehicle reaches any one of the four conditions, the probability that the vehicle is in the dangerous state is 1, the probability that the vehicle is in the other four vehicle distance states is 0 or a certain probability value between 0 and 1, and the specific probability value is what, the device can obtain the distance value, the collision time value of the two vehicles and the expected speed value of the vehicle by inquiring an experience driving speed table according to the safe distance value.

For another example, the probability that the host vehicle is in the distance risk state is not 1 in 1 out of the following 2 conditions. 2. The expected speed value of the vehicle is less than the speed of the vehicle, the difference is greater than or equal to a preset second difference, and the preset second difference is a certain determined value between 3 and 10 km/h. If the vehicle meets the two conditions at the same time, the probability that the vehicle is in the stable speed reduction state is 1, the probability that the vehicle is in the other four vehicle distance states is 0 or a certain probability value between 0 and 1, and the specific probability value is what number, and the probability value can be obtained by inquiring an empirical driving speed table according to the safe distance value, the collision time value of the two vehicles and the expected vehicle speed value of the vehicle.

Therefore, the experienced driving speed table in the related technology has a large number of conditions and results set manually, on one hand, rules set by different people are different, the obtained experienced driving speed tables are different, on the other hand, the driving speed determined according to uncertain rules is low in accuracy.

Based on the above, the application provides a vehicle distance keeping speed planning method based on fuzzy inference true value evolution.

To facilitate understanding of the present application, a description will first be given of the principle followed by the vehicle distance maintaining speed planning method based on the evolution of the fuzzy inference true value of the present application. In the effective inference process, the true value should be non-increasing, that is, the result of the next inference is based on the previous inference, and the inference process cannot create new knowledge (true value), only equal amount of knowledge (true value) may be retained or a part of knowledge (true value) is missed, so that the knowledge (true value) contained in the next inference cannot be more than that in the previous inference and should be non-increasing.

In particular, for a continuous reasoning process T ₁ ，T ₂ ，T ₃ ，…，T _n Wherein, T _k (k =1,2,3, …, n) is an "If A, tConditional proposition of hen B' form, whose true value is implied by fuzziness (Fuzzy inference) I (a) _k ,b _k ) Is given in which a _k Is the truth value of the conditional propositional antecedent A, b _k Is the truth value of the conditional proposition back-piece B, then in the effective reasoning process, the truth value I (a) _k ,b _k ) Should not be increased.

According to the vehicle distance keeping speed planning method based on the fuzzy inference true value evolution, under the condition that a vehicle is an expected acceleration, a vehicle speed error and a vehicle distance error are reduced and tend to 0 as an inference target. Firstly, a current vehicle distance error between a current vehicle distance and an expected vehicle distance and a current vehicle speed error between a current vehicle speed and an expected vehicle speed are obtained. And determining the vehicle distance error membership degree corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error, and determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error. And determining the acceleration which enables the value of the inference loss function to be in the minimum value according to the vehicle distance error membership, the vehicle speed error membership, the membership empirical function, the membership function corresponding to the acceleration and the inference loss function, and taking the acceleration as the expected acceleration. The membership degree empirical function is used for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error, and the inference loss function is used for reflecting the inference degree of the expected acceleration to the vehicle speed error and the inference reliability of the vehicle distance error. Finally, the vehicle running acceleration is adjusted to the expected acceleration.

According to the vehicle distance keeping speed planning method based on the fuzzy inference true value evolution, the value of the inference loss function is in the minimum value due to the determined expected acceleration, so that the process of determining the expected acceleration meets the effective inference condition that the true value is not increased, namely, the true value is not increased in the effective inference process, so that the determined expected acceleration is more accurate relative to the related technology, and the accuracy of vehicle speed planning is higher when the vehicle speed planning is carried out based on the determined expected acceleration.

The vehicle distance keeping speed planning method based on the fuzzy reasoning true value evolution can be applied to computer equipment integrated on a vehicle, and can also be computer equipment on other automatic driving equipment, such as a bicycle, an electric bicycle, automatic driving Internet of things equipment, an unmanned aerial vehicle, an unmanned vehicle and the like.

The application provides a vehicle distance keeping speed planning method based on fuzzy inference true value evolution, and a vehicle distance keeping speed planning device, equipment, a computer readable storage medium and a computer program product based on fuzzy inference true value evolution, which correspond to the vehicle distance keeping speed planning method based on the fuzzy inference true value evolution. Firstly, the method for planning the vehicle distance keeping speed based on the evolution of the fuzzy inference true value provided by the application is explained in detail.

In one embodiment, as shown in fig. 1, there is provided a method for vehicle distance keeping speed planning based on evolution of fuzzy inference true value, comprising the following steps:

step 101, obtaining a current vehicle distance error between a current vehicle distance and an expected vehicle distance and a current vehicle speed error between a current vehicle speed and an expected vehicle speed.

Wherein the current vehicle distance is the distance between the vehicle and the front vehicle. The expected vehicle distance is the safe distance that the vehicle is expected to maintain from the former. The current vehicle speed is the current running speed of the vehicle. The expected vehicle speed is a vehicle expected travel speed of the vehicle, i.e., a vehicle travel speed to which the adjustment should be made last, typically a current travel speed of a preceding vehicle.

In one embodiment, the equipment firstly obtains the expected distance between vehicles and the current distance between the vehicles and the front vehicle, and then obtains the current distance error between the current distance and the expected distance according to the difference value between the current distance between the vehicles and the front vehicle and the expected distance.

Specifically, the device may obtain an expected vehicle distance between the vehicle and the former vehicle from the vehicle expected speed input by the user, or determine a safe vehicle distance from the preceding vehicle according to the vehicle speed of the preceding vehicle, and determine the determined safe vehicle distance as the expected vehicle speed. The equipment can determine the distance between the current vehicle and the previous vehicle through a shooting system, an infrared system and the like of the vehicle to obtain the current distance.

In one embodiment, the device may first obtain a current vehicle speed and an expected vehicle speed, and then obtain a vehicle speed error between the current vehicle speed and the expected vehicle speed according to a difference between the current vehicle speed and the expected vehicle speed.

Specifically, the device determines the current running speed of the vehicle through an acceleration sensor of the vehicle, and the current running speed of the vehicle is used as the current vehicle speed, or the device can determine the moving distance of the vehicle in unit time according to the GPS running track, and further calculate the current vehicle speed of the vehicle. The device can determine the distance change rate between the vehicle and the front vehicle through a shooting system and the like, then obtain the current running speed of the front vehicle according to the current running speed of the vehicle, and take the current running speed of the front vehicle as the expected vehicle speed of the vehicle.

And 103, determining the vehicle distance error membership degree corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error, and determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error.

Wherein, the membership degree belongs to the concept in the fuzzy evaluation function: the fuzzy comprehensive evaluation is a very effective multi-factor decision method for comprehensively evaluating things influenced by various factors, and is characterized in that the evaluation result is not absolutely positive or negative, but is represented by a fuzzy set. If any element x in the domain of interest (the range under study) U has a number A (x) epsilon [0,1] corresponding to it, then A is called the fuzzy set on U, and A (x) is called the membership of x to A. When x varies among U, A (x) is a function called the membership function of A. The closer the degree of membership A (x) is to 1, the higher the degree to which x belongs to A, and the closer A (x) is to 0, the lower the degree to which x belongs to A. In the application, the membership function corresponding to the vehicle distance error is used for reflecting the corresponding relation between the vehicle distance error and the vehicle distance error fuzzy set, and the larger the vehicle distance error is, the larger the probability of membership to the vehicle distance error fuzzy set is (namely, the larger the membership corresponding to the vehicle distance error is). And the membership function corresponding to the vehicle speed error is used for reflecting the corresponding relation between the vehicle speed error and the vehicle speed error fuzzy set, and the probability of membership to the vehicle speed error fuzzy set is higher when the vehicle speed error is larger (namely the membership corresponding to the vehicle speed error is higher). The membership function corresponding to the expected acceleration is used for reflecting the corresponding relation between the acceleration and the acceleration fuzzy set, and the higher the acceleration is, the higher the probability of membership to the acceleration fuzzy set is (namely, the higher the membership of the acceleration to the acceleration is).

Specifically, after the device obtains the current vehicle distance error and the current vehicle speed error, the device substitutes the current vehicle distance error into the membership function corresponding to the vehicle distance error to obtain the vehicle distance error membership corresponding to the current vehicle distance error, and substitutes the current vehicle speed error into the membership function corresponding to the vehicle speed error to obtain the vehicle speed error membership corresponding to the current vehicle speed error.

And 105, determining the acceleration which enables the value of the inference loss function to be at the minimum value according to the vehicle distance error membership, the vehicle speed error membership, the membership empirical function, the membership function corresponding to the acceleration and the inference loss function, and taking the acceleration as the expected acceleration of the vehicle.

The membership degree empirical function is used for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error, the inference loss function is used for reflecting the inference degree from the expected acceleration to the inference credibility of the vehicle speed error and the vehicle distance error, when the inference loss function is at the minimum value, the inference credibility loss degree is minimum, and the accuracy of the inference result is higher.

The inference loss function is generally a function of the truth value of the front part and the truth value of the rear part with respect to the proposition of inference conditions, which are denoted as If the acceleration of the vehicle is a and the inter-vehicle distance error e _x And vehicle speed error e _v Will decrease and tend to 0', and the truth function corresponding to the acceleration of the vehicle of the front piece being a is mu _F (a) Rear part "vehicle spacing error e _x And vehicle speed error e _v The corresponding truth function which is reduced and tends to 0 ' is f (x), and the Lukasiewicz type fuzzy inclusion is selected as the truth value of the condition proposition of the ' if-then ', namely the truth value of the condition proposition of the ' if-then ' is z = I (x, f (x)) =1-x + xf (x).

Specifically, after the equipment determines the vehicle speed error membership degree and the vehicle distance error membership degree, the vehicle speed error membership degree and the vehicle distance error membership degree are substituted into a membership degree empirical function to obtain an acceleration membership degree of the expected acceleration, at the moment, the vehicle speed error membership degree, the vehicle distance error membership degree and the acceleration membership degree are substituted into an inference loss function to enable the inference loss function to be in a minimum value, then the acceleration membership degree is substituted into a membership degree function of the expected acceleration, and the obtained acceleration is determined as the expected acceleration of the vehicle.

In one embodiment, after determining the vehicle speed error membership degree corresponding to the current vehicle speed error and the vehicle distance error membership degree corresponding to the current vehicle distance error, the device determines the acceleration membership degree corresponding to the expected acceleration according to the corresponding relationship (the membership degree empirical function) among the vehicle speed error membership degree, the vehicle distance error membership degree and the acceleration membership degree, and then substitutes the determined acceleration membership degree into the membership degree function corresponding to the expected acceleration to obtain the expected acceleration of the vehicle.

In one embodiment, after the device determines the expected acceleration of the vehicle, the device sends an instruction corresponding to the expected acceleration to the connected steer-by-wire, accelerator, brake, and the like, so that the running acceleration of the vehicle is adjusted to the expected acceleration in cooperation with the steer-by-wire, accelerator, brake, and the like.

The vehicle distance keeping speed planning method based on the fuzzy inference true value evolution does not depend on an empirical running speed table, and determines the expected acceleration which enables the value of the inference loss function to be in the minimum value through establishing a reasonable inference process, so that the process of determining the expected acceleration meets the effective inference condition that the true value is not increased, namely, the true value is not increased in the effective inference process, the determined expected acceleration is more accurate relative to the related technology, and the accuracy of vehicle speed planning is higher when the vehicle speed planning is carried out based on the method.

In one embodiment, the apparatus may periodically perform the above steps 101 to 105, for example, every 1s, obtain a current vehicle distance error between the current vehicle distance and the expected vehicle distance and a current vehicle speed error between the current vehicle speed and the expected vehicle speed, and then perform

steps

103 and 105. As the vehicle adjusts acceleration and travels according to the expected acceleration, the vehicle speed of the vehicle, the vehicle distance error with the preceding vehicle, and the vehicle speed error with the expected vehicle speed will change, and the expected acceleration of the plant plan will change accordingly.

In one embodiment, before executing step 105, the method for vehicle distance keeping speed planning based on fuzzy inference true value evolution further includes:

and step 109, acquiring historical driving experience data of the vehicle.

The historical experience data comprises acceleration, vehicle distance error and vehicle speed error.

In one embodiment, the device can collect relevant data of experienced drivers when driving the vehicle, and process the collected relevant data to obtain historical driving experience data.

Specifically, the device collects the vehicle acceleration, the vehicle speed, the inter-vehicle distance from the preceding vehicle, and the vehicle speed of the preceding vehicle every 1 s. Then, the equipment processes a piece of driving data collected in each period, determines a vehicle speed error according to a difference value between the vehicle speed and the vehicle speed in front, determines a difference value between the vehicle distance between the vehicle speed and the vehicle speed in front and the vehicle distance finally kept as a vehicle distance error, and obtains a piece of driving experience data including acceleration, the vehicle distance error and the vehicle speed error.

And step 111, respectively determining the acceleration, the vehicle distance error, the historical acceleration membership of the vehicle speed error, the historical vehicle distance error membership and the historical vehicle speed error membership in the historical driving experience data according to the membership function corresponding to the vehicle distance error, the membership function corresponding to the vehicle speed error and the membership function corresponding to the acceleration.

For the membership function corresponding to the vehicle distance error, the membership function corresponding to the vehicle speed error, and the membership function corresponding to the acceleration, reference is made to the description in step 103.

Specifically, after the equipment acquires the driving experience data, the acceleration in the historical driving experience data is substituted into a membership function corresponding to the expected acceleration to obtain historical acceleration membership corresponding to the acceleration in the historical driving experience data, the vehicle distance error in the historical driving experience data is substituted into a membership function corresponding to the vehicle distance error to obtain historical vehicle distance error membership corresponding to the historical driving data, and the vehicle speed error in the historical driving experience data is substituted into a membership function corresponding to the vehicle speed error to obtain historical vehicle speed error membership corresponding to the vehicle speed error in the historical driving experience data.

And step 113, fitting to obtain a membership degree empirical function for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error according to the membership degree of the historical acceleration, the membership degree of the historical vehicle distance error and the membership degree of the historical vehicle speed error.

Specifically, the equipment obtains the historical acceleration membership, the historical vehicle distance error membership and the historical vehicle speed error membership corresponding to each acceleration, vehicle distance error and vehicle speed error in each piece of historical empirical driving data to obtain the membership data corresponding to each piece of historical empirical driving data. And then, the equipment performs interpolation fitting on the multiple groups of membership degree data by means of polynomial interpolation to obtain a membership degree empirical function for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error.

For example, each empirical datum is (e) _v ,e _x A), and corresponding three membership functions mu _F (a)，μ _G (e _x )，μ _H (e _v ) Obtaining membership grade data (mu) corresponding to each empirical function _F (a)，μ _G (e _x )，μ _H (e _v ) Then, the sets (μ) are interpolated by means of polynomials _F (a)，μ _G (e _x )，μ _H (e _v ) Carry out interpolation, the interpolated continuous transmission function mu _F (a)＝h(μ _G (e _x )，μ _H (e _v ) Function h () is of the form:

h(x,y)＝P ₀₀ +P ₁₀ x+P ₀₁ y+P ₁₁ xy+P ₂₀ x ² +P ₀₂ y ² +P ₂₁ x ² y+P ₁₂ xy ² +P ₃₀ x ³ +P ₀₃ y ³

in this embodiment, a membership degree empirical function is obtained by fitting the historical driving data, so that the membership degree of the expected acceleration that meets the historical empirical driving data is inferred according to the membership degree empirical function.

In one embodiment, before performing step 103, the method further comprises:

and step 115, acquiring boundary parameters of the expected acceleration, the vehicle speed error and the vehicle distance error respectively.

Specifically, the user may directly set boundary parameters of the desired acceleration, the vehicle speed error, and the vehicle distance error in the device in advance, and the device may read the boundary parameters of the desired acceleration, the vehicle speed error, and the vehicle distance error set by the user, respectively, to obtain the boundary parameters of the desired acceleration, the vehicle speed error, and the vehicle distance error. The device can also determine the boundary parameter of the expected acceleration by acquiring the vehicle data and the environment data and utilizing the corresponding relation between the vehicle data, the environment data and the acceleration boundary parameter. The equipment can also acquire sensitivity parameters, and determines boundary parameters of the vehicle speed error and the vehicle distance error according to the corresponding relation of the sensitivity parameters and the boundary parameters of the vehicle speed error and the vehicle distance error.

And step 117, constructing a function of the boundary parameter corresponding to the vehicle speed error as a membership function of the vehicle speed error.

Specifically, the equipment acquires a function that the vehicle speed error accords with a boundary parameter corresponding to the construction boundary parameter and the vehicle speed error accords with a membership degree, for example, when the vehicle speed error is more than 4m/s, the vehicle speed error is certain to be subordinate to a vehicle speed error fuzzy set, when the vehicle speed error is less than minus 4m/s, the vehicle speed error is certain not to be subordinate to the vehicle speed error fuzzy set, the vehicle speed error is proportional to the membership degree, and then the membership degree function for recording the vehicle speed error is mu _H (e _v ) Then μ _H (e _v ) Can be as follows:

wherein, mu _H (e _v ) The function form of (1) can also be other forms, and only needs to be a function which meets the boundary parameter of 4m/s to-4 m/s and has the vehicle speed error in direct proportion to the membership degree.

And step 119, constructing a function of the boundary parameter corresponding to the vehicle distance error as a membership function of the vehicle distance error.

Specifically, the equipment acquires a function that the vehicle distance error conforms to and constructs a boundary parameter corresponding to the vehicle distance error, and the vehicle distance error is in direct proportion to the membership degree, for example, when the vehicle distance error is greater than 10m, the vehicle distance error is certain to be subordinate to a vehicle distance error fuzzy set, when the vehicle distance error is less than minus 10m, the vehicle distance error is certain not to be subordinate to the vehicle distance error fuzzy set, the vehicle distance error is in direct proportion to the membership degree, and then the membership degree function for recording the vehicle distance error is mu _G (e _x ) Then μ _G (e _x ) Can be as follows:

wherein, mu _G (e _x ) The function form of (2) can also be other forms, and only needs to be a function which meets the boundary parameter of 10-10 m and has the vehicle distance error in direct proportion to the membership degree.

And 121, constructing a function of the boundary parameter corresponding to the expected acceleration as a membership function corresponding to the expected acceleration.

Specifically, the device obtains a function that the expected acceleration is in accordance with the boundary parameter corresponding to the constructed boundary parameter in accordance with the expected acceleration and the expected acceleration is in direct proportion to the membership degree, for example, when the expected acceleration is more than 5m/s ² The expected acceleration is bound to the fuzzy set of acceleration, and the expected acceleration is less than minus 5m/s ² When it is desired toThe acceleration is not necessarily attached to the acceleration fuzzy set, the expected acceleration is in direct proportion to the membership degree, and then the membership degree function of the vehicle speed error is recorded as mu _F (a) Then μ _F (a) Can be as follows:

wherein, mu _F (a) The function form of (A) can also be other forms, only the condition that the boundary parameter is 5m/s is met ² To-5 m/s ² And acceleration is a function proportional to the degree of membership.

In this embodiment, according to the boundary parameter, a function that meets the boundary parameter is determined, and membership functions corresponding to the expected acceleration, the vehicle speed error and the vehicle distance error are respectively constructed.

In an embodiment, the obtaining of the boundary parameter of the desired acceleration in step 115 specifically includes:

step A1, vehicle data and environment data are obtained.

The vehicle data may comprise, among other things, parameters relating to safe acceleration, such as vehicle weight, maximum acceleration. The environmental data may include environmental data about the acceleration affecting the safety of the vehicle, such as road humidity, weather visibility, acceleration speed limit of the road being driven, etc.

Specifically, the device may query the correspondence between the vehicle signal and the vehicle data stored locally to obtain the vehicle data corresponding to the vehicle, or the server may request the vehicle data corresponding to the vehicle model, and send the vehicle data to the device after the vehicle data is obtained by the server. The device may send the current geographic location information of the vehicle to the server, and the server queries the environmental data corresponding to the geographic location information and then sends the queried environmental data to the device. The device can also shoot the surrounding environment of the vehicle through a shooting system of the vehicle, and then the environmental data around the vehicle is determined by utilizing a preset recognition algorithm.

And A2, determining an upper limit acceleration value and a lower limit acceleration value corresponding to the expected acceleration according to the vehicle data and the environment data, and taking the upper limit acceleration value and the lower limit acceleration value as boundary parameters of the expected acceleration.

Specifically, after the device acquires the vehicle data and the environment data, the device queries the corresponding relationship between the vehicle data and the environment data and the upper limit acceleration and the lower limit acceleration, and determines the upper limit acceleration and the lower limit acceleration of the vehicle. The vehicle runs in the range between the upper limit acceleration and the lower limit acceleration, and the safety of the vehicle can be guaranteed.

In this embodiment, the user may set the boundary parameters of the vehicle acceleration according to actual conditions, or the device determines the boundary parameters that bring the vehicle within safe acceleration according to the vehicle data and the environmental data.

In an embodiment, the obtaining the boundary parameters of the vehicle speed error and the vehicle distance error in step 115 specifically includes:

and B1, acquiring sensitivity setting parameters.

Specifically, the device may acquire a default sensitivity setting parameter, or the user sets a sensitivity parameter when the vehicle is automatically driven on the device, and the device may read the sensitivity parameter set by the user.

And B2, determining boundary parameters of the vehicle speed error and the vehicle distance error according to the sensitivity setting parameters.

The sensitivity setting parameter is inversely proportional to the range of the boundary parameter of the vehicle speed error and the vehicle distance error, the smaller the range of the boundary parameter of the vehicle distance error is, the same the change of the membership degree of the vehicle speed error is, the larger the change of the vehicle speed error is, so the control on the vehicle is more sensitive, and the vehicle distance error is the same.

Specifically, the device may store a correspondence between the sensitivity parameter and boundary parameters of the vehicle speed error and the vehicle distance error, and after acquiring the sensitivity parameter, the device queries the correspondence between the sensitivity parameter and the boundary parameters of the vehicle speed error and the vehicle distance error to obtain the boundary parameters of the vehicle speed error and the vehicle distance error. The device can also pre-store the conversion relation between the sensitivity parameter and the boundary parameters of the vehicle speed error and the vehicle distance error, and after the device obtains the sensitivity parameter, the device converts the sensitivity parameter and the conversion relation between the sensitivity parameter and the boundary parameters of the vehicle speed error and the vehicle distance error to obtain the boundary parameters of the vehicle speed error and the vehicle distance error.

In the embodiment, the sensitivity parameters can be flexibly set to change the boundary parameters of the vehicle speed error and the vehicle distance error.

In an embodiment, the step 101 specifically includes:

step 101a, acquiring a current vehicle speed of the vehicle, a current vehicle distance between the vehicle and a preceding vehicle, a current vehicle speed of the preceding vehicle and an expected vehicle distance between the vehicle and the preceding vehicle.

Specifically, the equipment measures the distance between the current vehicle and the tail part of the front vehicle through vehicle-mounted sensors such as a vehicle integrated shooting system, infrared induction, sonar induction and millimeter wave radar to obtain the current vehicle distance. The device calculates the running speed of the front vehicle according to the change rate of the distance between the device and the front vehicle and the current vehicle speed of the vehicle. The device obtains the current running speed of the vehicle through the vehicle integrated acceleration sensor or a speed meter and the like, and obtains the current speed of the vehicle. And the equipment obtains the expected distance between the vehicle and the front vehicle according to the input of the user or the calculation of the safe distance.

And step 101b, determining a difference value between the current vehicle speed of the vehicle and the current vehicle speed of the previous vehicle as a current vehicle speed error, and determining a difference value between the current vehicle distance and the expected vehicle distance as a current vehicle distance error.

Specifically, after the device obtains the current vehicle speed of the vehicle and the current vehicle speed of the preceding vehicle, the current vehicle speed of the vehicle and the current vehicle speed of the preceding vehicle are subtracted to obtain a current vehicle speed error. After the equipment acquires the current vehicle distance and the expected vehicle distance, subtracting the current vehicle distance from the expected vehicle distance to obtain a current vehicle distance error.

A detailed description of one embodiment of the present application follows.

The longitudinal distance dt between the vehicles in front is recorded, the expected vehicle distance maintained by the vehicle distance is ds, the vehicle distance error ex is defined as ex = dt-ds, the vehicle speed of the vehicle in front is defined as v1, the vehicle speed of the vehicle is defined as v2, and the vehicle speed error ev is defined as ev = v2-v1.

The "if-then" conditional topic that defines fuzzy inference is:

"If the acceleration of the vehicle is a and the inter-vehicle distance error e _x And vehicle speed error e _v Will decrease and tend towards 0".

Instead, the planned target is described in the if front part, i.e. the acceleration (equivalent to the vehicle speed), and in the then rear part.

The truth value function corresponding to the front-part 'the acceleration of the vehicle is a' is mu _F (a) Rear part "vehicle spacing error e _x And vehicle speed error e _v The corresponding truth function which is reduced and tends to 0 ' is f (x), and the Lukasiewicz type fuzzy inclusion is selected as the truth value of the condition proposition of the ' if-then ', namely the truth value of the condition proposition of the ' if-then ' is z = I (x, f (x)) =1-x + xf (x).

The expected acceleration fuzzy set F is recorded as 'positive large' aiming at the expected acceleration a, namely, the larger the acceleration a is, the larger the probability that the acceleration a is subjected to membership grade acceleration fuzzy combination F is, and the membership grade function of the acceleration a subjected to the acceleration fuzzy set F is recorded as mu _F (a) In that respect Recording the fuzzy set G of the vehicle distance error as the vehicle distance error e _x Positive and large, vehicle distance error e _x The larger the probability of fuzzy combination G of membership degree vehicle distance errors is, the larger the vehicle distance error e _x The membership function for membership to the fuzzy set G is denoted as μ _G (e _x ). Recording the fuzzy set H of vehicle speed error as vehicle speed error e _v Positive, vehicle speed error e _v The greater the probability that membership degree vehicle speed error is combined with H in a fuzzy manner, the greater the vehicle speed error e _v The membership function to the fuzzy set H is denoted as mu _H (e _v )。

Definition of mu _F (a)、μ _G (e _x )、μ _H (e _v ) Is represented by the following three formulas:

each empirical data is (e) _v ,e _x A), and corresponding three membership functions mu _F (a)，μ _G (e _x )，μ _H (e _v ) Obtaining membership grade data (mu) corresponding to each empirical function _F (a)，μ _G (e _x )，μ _H (e _v ) Then, the sets (μ) are interpolated by means of polynomials _F (a)，μ _G (e _x )，μ _H (e _v ) Interpolation, the interpolated continuous transfer function:

μ _F (a)＝h(μ _G (e _x )，μ _H (e _v ) Function h () is of the form:

defining a condition proposition then truth function as:

then the true value of the conditional proposition is z = I (x, f (x)) =1-x + xf (x), and we can get:

when x = h (μ) _G (e _x )，μ _H (e _v ) In the case of a single-layer film),

designing a controller with Lyapunov stability:

u (t) controls the change rate of x (t), so that the truth value of the fuzzy inference process based on the condition proposition of the if-then is not increased, namely the control truth value z is not increased, and the control fuzzy inference process is stopped after the truth value z reaches the local minimum value, so as to avoid the condition that the truth value is increased due to continuous inference (at the local minimum value, the real value reaches a point which is larger than the local minimum value due to the continuous inference).

Specifically, make

u(t)＝h(μ _G (e _x )，μ _H (e _v ))-x

It can be shown that this control u (t) is able to control z to z _min I.e. the course of the entire z variation is non-increasing. It is specifically demonstrated that a state transformation needs to be performed on z, and bounded z is mapped to unbounded w, that is:

wherein,

z converges to z _min Equivalent to w converging to 0, by demonstrating w =0 lyapunov stability is equivalent to demonstrating z = z _min The stability proves the basic scientific basis that the inference truth value is not increased.

The time derivative can be:

the Lyapunov function is selected as follows:

V(w)＝e ^w -w-1

the time derivative can be:

after finishing, the product can be obtained

Therefore, the temperature of the molten metal is controlled,

and is

If and only if x = h (μ) _G (e _x )，μ _H (e _v ) I.e. z = z) _min Occurs when. Thus proving that

Negative, i.e. z = z _min Stability of the cell. Controlling u (t) ultimately achieves the effect of controlling x to h (μ) _G (e _x )，μ _H (e _v ) The basic scientific basis that the truth value of the reasoning process is not increased is ensured to be met through the stability of the Lyapunov.

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the application also provides a vehicle distance keeping speed planning device based on fuzzy reasoning true value evolution, which is used for realizing the vehicle distance keeping speed planning method based on fuzzy reasoning true value evolution. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the method, so that the specific limitations in one or more embodiments of the following device for planning the vehicle distance keeping speed based on the evolution of the fuzzy inference true value can be referred to as the limitations on the method for planning the vehicle distance keeping speed based on the evolution of the fuzzy inference true value, and are not described herein again.

In one embodiment, as shown in fig. 2, there is provided a vehicle distance maintaining speed planning apparatus based on fuzzy inference true value evolution, including:

the obtaining module 201 is configured to obtain a current vehicle distance error between a current vehicle distance and an expected vehicle distance, and a current vehicle speed error between a current vehicle speed and an expected vehicle speed;

the membership degree determining module 203 is used for determining the membership degree of the vehicle distance error corresponding to the current vehicle distance error according to the membership degree function corresponding to the vehicle distance error; determining a vehicle speed error membership degree corresponding to the current vehicle speed error according to a membership function corresponding to the vehicle speed error;

an acceleration determining module 205, configured to determine, according to the vehicle distance error membership, the vehicle speed error membership, a membership empirical function, a membership function corresponding to the acceleration, and a inference loss function, an acceleration at which a value of the inference loss function is at a minimum value, as an expected acceleration; the membership degree empirical function is used for reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error; and the inference loss function is used for reflecting the loss degree of the inference reliability of the expected acceleration to the vehicle speed error and the vehicle distance error.

In one embodiment, the above apparatus further comprises:

an experience data obtaining module 209 (not shown in the figure) for obtaining the vehicle historical driving experience data; the historical experience data comprises acceleration, vehicle distance error and vehicle speed error;

a membership degree obtaining module 211 (not shown in the figure) configured to determine, according to a membership degree function corresponding to the vehicle distance error, a membership degree function corresponding to the vehicle speed error, and a membership degree function corresponding to the acceleration, a historical acceleration membership degree, a historical vehicle distance error membership degree, and a historical vehicle speed error membership degree of the acceleration, the vehicle distance error, and the vehicle speed error in the historical driving experience data, respectively;

and an empirical function constructing function 213 (not shown in the figure) for fitting to obtain an empirical function of the membership degree reflecting the relationship among the membership degree of the expected acceleration, the membership degree of the vehicle speed error and the membership degree of the vehicle distance error according to the membership degree of the historical acceleration, the membership degree of the historical vehicle distance error and the membership degree of the historical vehicle speed error.

In one embodiment, the above apparatus further comprises:

a boundary parameter obtaining module 215 (not shown in the figure) for obtaining boundary parameters of the expected acceleration, the vehicle speed error and the vehicle distance error respectively;

a vehicle speed error membership degree constructing function 217 (not shown in the figure) for constructing a function of which the boundary parameter accords with the boundary parameter corresponding to the vehicle speed error, and the function is used as the membership degree function of the vehicle speed error;

a vehicle distance error membership degree building function 219 (not shown in the figure) which builds a function of which the boundary parameter accords with the boundary parameter corresponding to the vehicle distance error and is used as the membership degree function of the vehicle distance error;

an acceleration membership constructing function 221 (not shown in the figure) for constructing a function of which the boundary parameter is in accordance with the boundary parameter corresponding to the desired acceleration as a membership function corresponding to the desired acceleration.

In an embodiment, the boundary parameter obtaining module 215 is specifically configured to:

acquiring vehicle data and environment data; and determining an upper limit acceleration value and a lower limit acceleration value corresponding to the expected acceleration according to the vehicle data and the environment data, and taking the upper limit acceleration value and the lower limit acceleration value as boundary parameters of the expected acceleration.

acquiring sensitivity setting parameters; determining boundary parameters of the vehicle speed error and the vehicle distance error according to the sensitivity setting parameters; the sensitivity setting parameter is inversely proportional to the range of the boundary parameter of the vehicle speed error and the vehicle distance error.

In an embodiment, the obtaining module 201 is specifically configured to:

acquiring the current speed of the vehicle, the current distance between the vehicle and a front vehicle, the current speed of the front vehicle and the expected distance between the vehicle and the front vehicle; and determining the difference between the current vehicle speed of the vehicle and the current vehicle speed of the preceding vehicle as a current vehicle speed error, and determining the difference between the current vehicle distance and the expected vehicle distance as a current vehicle distance error.

The modules in the above-mentioned vehicle distance maintaining speed planning device based on the evolution of the fuzzy inference true value can be wholly or partially realized by software, hardware and the combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a computer device integrated with a vehicle, and its internal structure diagram may be as shown in fig. 3. The computer device comprises a processor, a memory, and a communication interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for communicating with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method for vehicle distance keeping speed planning based on fuzzy inference truth value evolution.

Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A vehicle distance keeping speed planning method based on fuzzy inference true value evolution is characterized by comprising the following steps:

determining the vehicle distance error membership degree corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error; determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error;

2. The method of claim 1, wherein before determining the acceleration that minimizes the inference loss function value based on the vehicle distance error membership, the vehicle speed error membership, a membership empirical function, a membership function to the acceleration, and an inference loss function, the method further comprises:

3. The method according to claim 1, characterized in that the vehicle distance error membership degree corresponding to the current vehicle distance error is determined according to a membership function corresponding to the vehicle distance error; before determining the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error, the method further comprises the following steps:

respectively acquiring boundary parameters of the expected acceleration, the vehicle speed error and the vehicle distance error;

and constructing a function of the boundary parameter corresponding to the expected acceleration as a membership function corresponding to the expected acceleration.

4. The method of claim 3, wherein obtaining boundary parameters of the desired acceleration comprises:

acquiring vehicle data and environment data;

5. The method according to claim 3, wherein the obtaining boundary parameters of the vehicle speed error and the vehicle distance error comprises:

acquiring sensitivity setting parameters;

6. The method of claim 1, wherein obtaining a current vehicle-to-vehicle distance error between a current vehicle-to-vehicle distance and an expected vehicle-to-vehicle distance and a current vehicle speed error between a current vehicle speed and an expected vehicle speed comprises:

7. A distance maintenance speed planning apparatus based on fuzzy inference true value evolution, the apparatus comprising:

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.

9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.