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

Abstract

The present application relates to a speed planning method, device, computer equipment, storage medium and computer program product based on the evolution of the true value of fuzzy inference. The method includes: obtaining the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed. According to the membership function corresponding to the vehicle distance error, the vehicle distance error membership degree corresponding to the current vehicle distance error is determined, and the vehicle speed error membership degree corresponding to the current vehicle speed error is determined according to the membership function corresponding to the vehicle speed error. According to the membership degree of the distance error, the membership of the vehicle speed error, the membership experience function, the membership function corresponding to the acceleration, and the inference loss function, the acceleration that makes the value of the inference loss function at the minimum value is determined as the expected acceleration. Through the method of the present application, the accuracy of the expected acceleration can be improved.

Description

A Velocity Planning Method Based on Fuzzy Inference Truth Value Evolution

technical field

The present application relates to the technical field of automatic driving, in particular to a method for planning distance between vehicles based on truth-value evolution of fuzzy reasoning.

Background technique

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

In related technologies, the empirical driving speed table is generally established through the historical driving data of the vehicle, that is, when the speed of the vehicle in front, the distance between the vehicle in front and the speed of the vehicle in front are respectively in a certain value range, the speed table should be adjusted. The vehicle acceleration is some expected acceleration. Among them, the empirical speedometers established by different people are quite different, therefore, the accuracy of manually establishing the empirical speedometers is low.

Since the accuracy of the establishment of the empirical speed table directly determines the accuracy of the expected acceleration, the accuracy of the expected acceleration determined in the related art is also low, and the accuracy of the vehicle speed planning based on this is also low.

Contents of the invention

Based on this, it is necessary to address the above-mentioned technical problems and provide a method, device, computer equipment, computer-readable storage medium and Computer Program Products.

In a first aspect, the present application provides a vehicle speed planning method. The methods include:

Obtain the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed;

According to the membership degree function corresponding to the vehicle distance error, determine the vehicle distance error membership degree corresponding to the current vehicle distance error; according to the vehicle speed error corresponding membership degree function, determine the vehicle speed error membership degree corresponding to the current vehicle speed error;

According to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration and the inference loss function, determine the acceleration that makes the value of the inference loss function at a minimum value, as The expected acceleration of the vehicle; the membership degree empirical function is used to reflect the relationship between 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 reasoning loss function is used for Reflecting the degree of loss of inference reliability from the expected acceleration to the vehicle speed error and the vehicle distance error.

In one of the embodiments, according to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration and the inference loss function, it is determined that the value of the inference loss function is between The acceleration of the minimum value, as the expected acceleration, the method also includes:

Acquiring historical driving experience data of the vehicle; the historical experience data includes acceleration, vehicle distance error, and vehicle speed error;

According to the membership degree function corresponding to the vehicle distance error, the membership degree function corresponding to the vehicle speed error, and the membership degree function corresponding to the acceleration, respectively determine the acceleration, vehicle distance error, and vehicle speed error in the historical driving experience data. The membership degree of historical acceleration, the membership degree of historical vehicle distance error, and the membership degree of historical vehicle speed 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, the membership degree for reflecting the expected acceleration, the membership degree of the vehicle speed error, and the vehicle speed error membership degree are obtained by fitting. The membership degree empirical function of the relationship between the membership degrees from the error.

In one of the embodiments, according to the membership function corresponding to the vehicle distance error, the vehicle distance error membership degree corresponding to the current vehicle distance error is determined; according to the vehicle speed error corresponding membership degree function, the current vehicle speed error is determined. Before the vehicle speed error membership degree, the method also includes:

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

Constructing a function whose boundary parameter conforms to the boundary parameter corresponding to the vehicle speed error, as the membership function of the vehicle speed error;

Constructing a function whose boundary parameter conforms to the boundary parameter corresponding to the vehicle distance error, as the membership function of the vehicle distance error;

A function whose boundary parameters conform to the boundary parameters corresponding to the expected acceleration is constructed as a membership function corresponding to the expected acceleration.

In one of the embodiments, the acquisition of the boundary parameters of the expected acceleration includes:

Obtain vehicle data and environmental data;

According to the vehicle data and the environment data, an upper limit acceleration value and a lower limit acceleration value corresponding to the expected acceleration are determined, and the upper limit acceleration value and the lower limit acceleration value are used as boundary parameters of the expected acceleration.

In one of the embodiments, the acquisition of boundary parameters of vehicle speed error and vehicle distance error includes:

Get the sensitivity setting parameters;

Determine the boundary parameters of the vehicle speed error and the vehicle distance error according to the sensitivity setting parameters; the sensitivity setting parameters are inversely proportional to the ranges of the vehicle speed error and the boundary parameters of the vehicle distance error.

In one of the embodiments, the acquisition of the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed includes:

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

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

In the second aspect, the present application also provides a vehicle distance keeping speed planning device based on fuzzy reasoning truth evolution. The devices include:

An acquisition module, configured to acquire the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed;

The membership determination module is used to determine the vehicle distance error membership corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error; and determine the vehicle speed corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error Error membership degree;

The acceleration determination module is used to determine the value of the inference loss function to be in The acceleration of the minimum value is used as the expected acceleration of the vehicle; the membership empirical function is used to reflect the relationship between the membership of the desired acceleration, the membership of the vehicle speed error, and the membership of the vehicle distance error; The inference loss function is used to reflect the degree of loss of inference credibility from the expected acceleration inference 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 includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

Obtain the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed;

According to the membership degree function corresponding to the vehicle distance error, determine the vehicle distance error membership degree corresponding to the current vehicle distance error; according to the vehicle speed error corresponding membership degree function, determine the vehicle speed error membership degree corresponding to the current vehicle speed error;

According to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration and the inference loss function, determine the acceleration that makes the value of the inference loss function at a minimum value, as The expected acceleration of the vehicle; the membership degree empirical function is used to reflect the relationship between 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 reasoning loss function is used for Reflecting the degree of loss of inference reliability from the expected acceleration to the vehicle speed error and the vehicle distance error.

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

Obtain the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed;

According to the membership degree function corresponding to the vehicle distance error, determine the vehicle distance error membership degree corresponding to the current vehicle distance error; according to the vehicle speed error corresponding membership degree function, determine the vehicle speed error membership degree corresponding to the current vehicle speed error;

According to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration and the inference loss function, determine the acceleration that makes the value of the inference loss function at a minimum value, as The expected acceleration of the vehicle; the membership degree empirical function is used to reflect the relationship between 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 reasoning loss function is used for Reflecting the degree of loss of inference reliability from the expected acceleration to the vehicle speed error and the vehicle distance error.

In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

Obtain the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed;

According to the membership degree function corresponding to the vehicle distance error, determine the vehicle distance error membership degree corresponding to the current vehicle distance error; according to the vehicle speed error corresponding membership degree function, determine the vehicle speed error membership degree corresponding to the current vehicle speed error;

According to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration and the inference loss function, determine the acceleration that makes the value of the inference loss function at a minimum value, as The expected acceleration of the vehicle; the membership degree empirical function is used to reflect the relationship between 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 reasoning loss function is used for Reflecting the degree of loss of inference reliability from the expected acceleration to the vehicle speed error and the vehicle distance error.

The above-mentioned method, device, computer equipment, storage medium and computer program product based on the fuzzy inference truth value evolution of vehicle distance keeping speed planning, use the non-increasing method of fuzzy inference truth value, and obtain the expected acceleration by reasoning. Compared with the method in the related art , the determined expected acceleration rate is relatively high, according to the vehicle distance keeping speed planning method based on fuzzy reasoning truth evolution of the present application, the vehicle's expected acceleration is planned, and the vehicle distance keeping accuracy rate is also higher.

Description of drawings

Fig. 1 is a kind of schematic flow chart of the vehicle distance keeping speed planning method based on fuzzy inference truth evolution in one embodiment;

Fig. 2 is a structural block diagram of a vehicle distance keeping speed planning device based on fuzzy reasoning truth evolution in an embodiment;

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

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

In the related technology, it is generally equipped with an empirical driving speed table, and the empirical driving speed table stores several conditions and corresponding expected accelerations. For example, the empirical driving speed table stores several vehicle distance states and several vehicle speed states, any safety In the case of the distance value, the collision time value of the two vehicles and the expected vehicle speed value of the own vehicle, the corresponding probability values of the own vehicle being in each vehicle distance state and the probability values of the own vehicle being in each vehicle speed state. The empirical driving speed table also stores a number of vehicle operating conditions, preset jerk values corresponding to each vehicle operating condition, vehicle distance status and vehicle speed status corresponding to each vehicle operating condition.

The device calculates the probability values of each vehicle working condition of the vehicle through the preset vehicle working condition probability algorithm according to the probability value of the vehicle being in each vehicle distance state and the probability value of the vehicle being in each vehicle speed state, and then according to The safety distance value, the collision time value of the two vehicles and the expected vehicle speed value of the vehicle refer to the empirical driving speed table to determine the probability value of the vehicle in each vehicle distance state and the probability value of the vehicle in each vehicle speed state, and then according to each vehicle The expected jerk value of the vehicle is obtained by using the state probability value and the preset acceleration value of each vehicle working state.

Specifically, among the following four conditions: 1. The distance between the vehicle in front is less than a preset distance value, and the preset distance value is a certain value between 5-20 meters. 2. The collision time between the host vehicle and the preceding vehicle is less than the preset time value, and the preset time value is a definite value between 3-10 seconds. 3. The ratio of the distance between the vehicle in front and the safety distance value ds is less than a preset threshold value, and the preset threshold value is a certain value between 30%-45%. 4. The expected vehicle speed value of the vehicle is less than the vehicle speed of the vehicle, and the difference is greater than or equal to the preset first difference value, and the preset first difference value is a certain value between 15-25km/h. If the vehicle meets any of the four conditions, the probability that the vehicle is in the distance danger state is 1, and the probability that the vehicle is in the other four vehicle distance states is 0 or a certain probability between 0-1 The value, the specific probability value, the device can be obtained by querying the empirical driving speed table according to the safety distance value, the collision time value of the two vehicles and the expected speed value of the vehicle.

For another example, in the following two conditions: 1. The probability that the vehicle is in a dangerous state of distance is not 1. 2. The expected speed of the vehicle is less than the speed of the vehicle, and the difference is greater than or equal to the preset second difference. The preset second difference is a certain value between 3-10km/h. If the vehicle meets these two conditions at the same time, the probability that the vehicle is in the state of stable distance deceleration is 1, and the probability that the vehicle is in the other four vehicle distance states is 0 or a certain probability value between 0-1 , the specific probability value can be obtained by querying the empirical driving speed table according to the safety distance value, the collision time value of the two vehicles and the expected speed value of the vehicle.

It can be seen that there are a large number of artificially set conditions and results in the empirical speedometer in the related art. On the one hand, the rules set by different people are not the same, and the empirical speedometers obtained are different. The driving speed determined by the rules has low accuracy.

Based on this, the present application proposes a speed planning method based on fuzzy inference truth-value evolution between vehicles to maintain distance.

In order to facilitate the understanding of the present application, firstly, the principles followed by the distance-maintaining speed planning method based on fuzzy inference truth-value evolution of the present application will be described. In an effective reasoning process, the truth value should be non-increasing, that is, the result of the next step of reasoning is based on the previous step of reasoning, and the reasoning process cannot create new knowledge (true value), only the same amount of knowledge (true value) can be retained. value) or a part of knowledge (truth value) is omitted, so the knowledge (truth value) contained in the reasoning in the latter step cannot be more than that in the previous step, and it should be non-increasing.

Specifically, for a continuous reasoning process T ₁ , T ₂ , T ₃ ,..., T _n , where T _k (k=1, 2, 3,..., n) is an "If A, then B" form The conditional proposition of , its truth value is given by the fuzzy implication (Fuzzy Implication) I(a _k ,b _k ), where a _k is the truth value of the antecedent A of the conditional proposition, b _k is the truth value of the consequent B of the conditional proposition, Then in the effective reasoning process, the truth value I(a _k , b _k ) should be non-increasing.

The vehicle distance maintenance speed planning method based on fuzzy inference truth evolution of the present application takes the vehicle as the expected acceleration, the vehicle speed error and the vehicle distance error will decrease and tend to 0 as the reasoning target. First, the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed are obtained. According to the membership degree function corresponding to the vehicle distance error, the vehicle distance error membership degree corresponding to the current vehicle distance error is determined, and according to the vehicle speed error corresponding membership degree function, the vehicle speed error membership degree corresponding to the current vehicle speed error is determined. According to the membership degree of vehicle distance error, vehicle speed error membership, membership experience function, membership function corresponding to acceleration and inference loss function, the acceleration that makes the value of the inference loss function at the minimum value is determined as the expected acceleration. Among them, the membership experience function is used to reflect the relationship between 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 reasoning loss function is used to reflect the reasoning from the expected acceleration to the vehicle speed error and vehicle distance error The degree of loss of credibility. Finally, the vehicle travel acceleration is adjusted to the expected acceleration.

Through the vehicle distance keeping speed planning method based on fuzzy inference truth evolution of the present application, since the determined expected acceleration makes the value of the inference loss function at the minimum value, the process of determining the expected acceleration conforms to the effective reasoning of the above-mentioned non-increasing truth value The condition, that is, in the effective reasoning process, the true value should not increase, so the determined expected acceleration is more accurate than that of the related technology, and the accuracy of the vehicle speed planning is also higher when the vehicle speed planning is based on this.

The method of vehicle distance keeping speed planning based on fuzzy inference truth evolution provided by the embodiment of the present application can be applied to the computer equipment integrated on the vehicle, or it can be the computer equipment on other automatic driving equipment, such as bicycles, motorcycles, automatic Drive IoT devices, drones, driverless vehicles, and more.

This application provides a vehicle distance keeping speed planning method based on fuzzy reasoning truth evolution, and a vehicle distance keeping speed based on fuzzy reasoning truth evolution corresponding to a fuzzy reasoning truth evolution based vehicle distance keeping speed planning method Design means, equipment, computer readable storage medium, computer program product. Firstly, a detailed description will be given on the distance-maintaining speed planning method based on fuzzy inference truth-value evolution provided by this application.

In one embodiment, as shown in Figure 1, a kind of vehicle distance keeping speed planning method based on fuzzy inference truth evolution is provided, comprising the following steps:

Step 101. Obtain the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed.

Wherein, the current vehicle distance is the distance between the vehicle and the preceding vehicle. The expected inter-vehicle distance is the safe distance between the vehicle and the former. The current vehicle speed is the current driving speed of the vehicle. The expected vehicle speed is the expected driving speed of the vehicle, that is, the driving speed of the vehicle to which it should be adjusted at last, and is generally the current driving speed of the preceding vehicle.

In one embodiment, the device first obtains the expected vehicle distance and the current vehicle distance to the vehicle in front, and then obtains the current vehicle distance based on the difference between the current vehicle distance and the expected vehicle distance between the vehicle and the vehicle in front The current distance error from the expected distance.

Specifically, the device can obtain the expected vehicle distance between the vehicle and the former through the expected vehicle speed input by the user, or determine the safe distance between the vehicle and the vehicle in front according to the speed of the vehicle in front, and convert the determined safe distance to the vehicle in front. The vehicle distance is determined as the expected vehicle speed. The device can determine the current distance between the vehicle and the vehicle in front through the vehicle's own camera system, infrared system, etc., and obtain the current distance.

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

Specifically, the device determines the current driving speed of the vehicle through the acceleration sensor that comes with the vehicle, and takes the current driving speed of the vehicle as the current speed, or can determine the moving distance of the vehicle per unit time according to the GPS driving track, and then calculate the vehicle speed. current speed. The device can determine the rate of change of the distance between the vehicle and the vehicle in front through the camera system, etc., and then obtain the current speed of the vehicle in front according to the current speed of the vehicle, and use the current speed of the vehicle in front as the expected speed of the vehicle.

Step 103 : Determine the vehicle distance error membership degree corresponding to the current vehicle distance error according to the membership degree function corresponding to the vehicle distance error, and determine the vehicle speed error membership degree corresponding to the current vehicle speed error according to the membership degree function corresponding to the vehicle speed error.

Among them, the degree of membership belongs to the concept in the fuzzy evaluation function: fuzzy comprehensive evaluation is a very effective multi-factor decision-making method to make a comprehensive evaluation of things affected by multiple factors, and its characteristic is that the evaluation result is not absolutely positive or negative , but represented by a fuzzy set. If there is a number A(x)∈[0, 1] corresponding to any element x in the domain of discourse (the scope of research) U, then A is called a fuzzy set on U, and A(x) is called is the membership degree of x to A. When x changes in 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 that x belongs to A, and the closer A(x) is to 0, the lower the degree that x belongs to A. In this application, the membership function corresponding to the vehicle distance error is used to reflect the corresponding relationship between the vehicle distance error and the vehicle distance error fuzzy set. The larger the vehicle distance error, the greater the probability of belonging to the vehicle distance error fuzzy set (ie, the vehicle distance The greater the degree of membership corresponding to the error). The membership function corresponding to the vehicle speed error is used to reflect the corresponding relationship between the vehicle speed error and the vehicle speed error fuzzy set. The membership function corresponding to the expected acceleration is used to reflect the corresponding relationship between the acceleration and the acceleration fuzzy set.

Specifically, after the device acquires the current vehicle distance error and the current vehicle speed error, the current vehicle distance error is substituted into the membership degree function corresponding to the vehicle distance error to obtain the vehicle distance error membership degree corresponding to the current vehicle distance error, and the current vehicle speed error Substitute into the membership degree function corresponding to the vehicle speed error to obtain the membership degree of the vehicle speed error corresponding to the current vehicle speed error.

Step 105, according to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration, and the inference loss function, determine the acceleration that makes the value of the inference loss function at a minimum value as the expected acceleration of the vehicle.

Among them, the membership experience function is used to reflect the relationship between 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 reasoning loss function is used to reflect the expected acceleration to the vehicle speed error and vehicle distance error. The degree of loss of inference credibility. When the inference loss function is at the minimum value, the loss of inference credibility is the smallest, and the accuracy of the inference result is higher.

The reasoning loss function is generally a function of the truth value of the antecedent and the truth value of the latter part of the reasoning conditional proposition. In this application, the reasoning conditional proposition is recorded as "If the acceleration of the vehicle is a, then the inter-vehicle distance error e _x and the vehicle speed The error e _v will decrease and tend to 0", the truth function corresponding to the former part "the acceleration of the vehicle is a" is μ _F (a), the latter part "the inter-vehicle distance error e _x and the vehicle speed error e _v will decrease and The truth function corresponding to tending to 0" is f(x), and the fuzzy implication is taken as the truth value of the "if-then" conditional proposition, and the Lukasiewicz type fuzzy implication is selected, that is, the truth value of the "if-then" conditional proposition is z= I(x,f(x))=1−x+xf(x).

Specifically, after the equipment determines the membership degree of vehicle speed error and the membership degree of vehicle distance error, the membership degree of vehicle speed error and the membership degree of vehicle distance error are substituted into the empirical membership function to obtain the acceleration membership degree of the expected acceleration. At this time, the vehicle speed error Substituting the membership degree, vehicle distance error membership degree, and acceleration membership degree into the inference loss function, so that the inference loss function is at a minimum value, and then substituting the acceleration membership degree into the membership degree function of the expected acceleration, and determining the obtained acceleration as the vehicle's expected acceleration.

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 equipment determines the vehicle speed error membership degree, vehicle distance error membership degree, and acceleration membership degree. Corresponding relationship (the above-mentioned membership degree experience function), determine the acceleration membership degree corresponding to the expected acceleration, and then substitute the determined acceleration membership degree into the membership degree function corresponding to the expected acceleration to obtain the expected acceleration of the vehicle. At this time, the expected acceleration The acceleration membership degree of the current vehicle speed error, 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 are substituted into the inference loss function, so that the inference loss function is at a minimum.

In one embodiment, after the device determines the expected acceleration of the vehicle, the device sends instructions corresponding to the expected acceleration to the connected steering by wire, accelerator, brake, etc., so that with the cooperation of steering by wire, accelerator, brake, etc., The driving acceleration of the vehicle is adjusted to the expected acceleration.

The vehicle distance keeping speed planning method based on fuzzy inference truth evolution provided by this application does not rely on the empirical speed table, but determines the expected acceleration that makes the value of the inference loss function at the minimum by establishing a reasonable inference process , so the process of determining the expected acceleration conforms to the effective reasoning condition of non-increasing true value, that is, in the effective reasoning process, the true value should be non-increasing, so the determined expected acceleration is more accurate than that in related technologies, and the vehicle speed is determined based on this When planning, the accuracy rate of vehicle speed planning is also high.

In one embodiment, the device can periodically execute the above step 101 to step 105, for example, every 1s, obtain the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed and the expected vehicle speed. Vehicle speed error, then execute step 103, step 105. As the vehicle adjusts the acceleration and drives according to the expected acceleration, the vehicle speed, the distance error between the vehicle in front and the expected vehicle speed will change accordingly, and the expected acceleration planned by the equipment will also change accordingly.

In one embodiment, before step 105 is performed, the above-mentioned vehicle distance keeping speed planning method based on fuzzy inference truth evolution also includes:

Step 109, acquiring historical driving experience data of the vehicle.

Among them, the historical experience data includes acceleration, vehicle distance error, and vehicle speed error.

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

Specifically, the device collects vehicle acceleration, vehicle speed, distance to the vehicle in front, and vehicle speed of the vehicle in front every 1s. Then, the device processes a piece of driving data collected in each cycle, determines the vehicle speed error from the difference between the vehicle speed and the speed of the preceding vehicle, and calculates the difference between the distance between the vehicle in front and the final maintained distance The value is determined as the vehicle distance error, and a piece of driving experience data including acceleration, vehicle distance error and vehicle speed error is obtained.

Step 111, according to the membership degree function corresponding to the vehicle distance error, the membership degree function corresponding to the vehicle speed error, and the membership degree function corresponding to the acceleration, respectively determine the historical acceleration membership degree, vehicle distance error, and vehicle speed error in the historical driving experience data. The membership degree of historical vehicle distance error and the membership degree of historical vehicle speed error.

Wherein, 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 can refer to the description of step 103 .

Specifically, after the device obtains the driving experience data, it substitutes the acceleration in the historical driving experience data into the membership degree function corresponding to the expected acceleration, and obtains the historical acceleration membership degree corresponding to the acceleration in the historical driving experience data. The vehicle distance error in is substituted into the membership function corresponding to the vehicle distance error, and the historical vehicle distance error membership degree corresponding to the historical driving data is obtained, and the vehicle speed error in the historical driving experience data is substituted into the membership degree function corresponding to the vehicle speed error, and the historical vehicle distance error is obtained. The membership degree of the historical vehicle speed error corresponding to the vehicle speed error in the driving experience data.

Step 113, according to the membership degree of historical acceleration, the membership degree of historical vehicle distance error, and the membership degree of historical vehicle speed error, the relationship between the membership degree used to reflect the expected acceleration, the membership degree of vehicle speed error, and the membership degree of vehicle distance error is obtained by fitting membership experience function.

Specifically, the device obtains the respective historical acceleration membership degree, historical vehicle distance error membership degree, and historical vehicle speed error membership degree corresponding to the acceleration, vehicle distance error, and vehicle speed error in each piece of historical experience driving data, and obtains each piece of historical experience driving data. Corresponding membership data. Afterwards, the device performs interpolation and fitting on multiple sets of membership degree data by means of polynomial interpolation, and obtains the membership degree experience function for reflecting the relationship between the membership degree of expected acceleration, the membership degree of vehicle speed error, and the membership degree of vehicle distance error.

For example, each piece of empirical data _is ( _ev ,ex ,a), and the corresponding three membership functions μ _F (a), μ _G ( _ex ), μ _H ( _ev ), and each empirical function The corresponding membership degree data (μ _F (a), μ _G ( _ex ), μ _H ( _ev )), after that, with the help of polynomial interpolation for multiple groups (μ _F (a), μ _G ( _ex ), μ _H (e _v )) for interpolation, the interpolated continuous transmission function μ _F (a) = h(μ _G (e _x ), μ _H (e _v )), the form of the function h(·,·) is:

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, the membership degree experience function is obtained by fitting according to the historical driving experience driving data, so that the membership degree of the expected acceleration conforming to the historical experience driving data is deduced according to the membership degree empirical function.

In one embodiment, before step 103 is performed, the method further includes:

Step 115 , obtaining boundary parameters of expected acceleration, vehicle speed error, and vehicle distance error respectively.

Specifically, the user can directly set the boundary parameters of expected acceleration, vehicle speed error, and vehicle distance error in the device in advance, and the device reads the boundary parameters of expected acceleration, vehicle speed error, and vehicle distance error set by the user respectively, and obtains the expected acceleration and vehicle speed error. , The boundary parameter of the vehicle distance error. The device can also determine the boundary parameters of the expected acceleration by acquiring the vehicle data and the environment data, and using the corresponding relationship between the vehicle data, the environment data and the acceleration boundary parameters. The device can also obtain the sensitivity parameters, and determine the boundary parameters of the vehicle speed error and the vehicle distance error through the corresponding relationship between the sensitivity parameters and the boundary parameters of the vehicle speed error and the vehicle distance error.

Step 117 , constructing a function whose boundary parameter conforms to the boundary parameter corresponding to the vehicle speed error, as a membership function of the vehicle speed error.

Specifically, the device obtains a function in which the vehicle speed error conforms to the construction boundary parameter and corresponds to the boundary parameter of the vehicle speed error, and the vehicle speed error is proportional to the degree of membership. For example, when the vehicle speed error is greater than 4m/s, the vehicle speed error must belong to the vehicle speed error fuzzy set, when the vehicle speed error is less than negative 4m/s, the vehicle speed error must not belong to the vehicle speed error fuzzy set, and the vehicle speed error is proportional to the degree of membership, then the membership degree function of the vehicle speed error is μ _H ( _ev ), then μ _H ( e _v ) can be:

Among them, the function form of μ _H ( _ev ) can also be in other forms, as long as it is a function that conforms to the boundary parameter of 4m/s to -4m/s, and the vehicle speed error is proportional to the degree of membership.

Step 119, constructing a function whose boundary parameters conform to the boundary parameters corresponding to the vehicle distance error, as a membership function of the vehicle distance error.

Specifically, the device obtains a function in which the vehicle distance error conforms to the construction boundary parameters and corresponds to the boundary parameters of the vehicle distance error, and the vehicle distance error is proportional to the degree of membership. For example, when the vehicle distance error is greater than 10m, the vehicle distance error must belong to The vehicle distance error fuzzy set, when the vehicle distance error is less than negative 10m, the vehicle distance error must not belong to the vehicle distance error fuzzy set, and the vehicle distance error is proportional to the membership degree, then the membership function of the vehicle distance error is μ _G (e _x ), then μ _G (e _x ) can be:

Among them, the function form of μ _G ( _ex ) can also be in other forms, as long as it meets the boundary parameters of 10 to -10m, and the inter-vehicle distance error is proportional to the degree of membership.

Step 121 , constructing a function whose boundary parameter meets the boundary parameter corresponding to the expected acceleration, as a membership function corresponding to the expected acceleration.

Specifically, the device obtains a function in which the expected acceleration conforms to the construction boundary parameters and the corresponding boundary parameters of the expected acceleration, and the expected acceleration is proportional to the degree of membership. For example, when the expected acceleration is greater than 5m/s ² , the expected acceleration must belong to the acceleration fuzzy set, when the expected acceleration is less ^than negative 5m/s2, the expected acceleration must not belong to the acceleration fuzzy set, and the expected acceleration is proportional to the degree of membership, then the membership function of the vehicle speed error is μ _F (a), then μ _F (a ) can be:

Among them, the function form of μ _F (a) can also be in other forms, as long as it meets the boundary parameters of 5m/s ² to -5m/s ² , and the acceleration is proportional to the degree of membership.

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

In one embodiment, obtaining the boundary parameters of the expected acceleration in the above step 115 specifically includes:

Step A1, acquiring vehicle data and environment data.

Wherein, the vehicle data may include parameters related to safe acceleration, such as vehicle weight and maximum acceleration. The environmental data may include relevant environmental data affecting the safe acceleration of the vehicle, such as road humidity, weather visibility, acceleration speed limit of the road on which the vehicle is traveling, and the like.

Specifically, the device can query the corresponding relationship between the vehicle signal and the vehicle data stored locally by the vehicle model, and obtain the vehicle data corresponding to the vehicle, or request the vehicle data corresponding to the vehicle model from the server, and after the server obtains the query, the vehicle Data is sent to the device. The device can 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 take pictures of the surrounding environment of the vehicle through the vehicle's own shooting system, and then use the preset recognition algorithm to determine the environmental data around the vehicle.

Step A2, according to the vehicle data and the environment data, determine the upper limit acceleration value and the lower limit acceleration value corresponding to the expected acceleration, and use the upper limit acceleration value and the lower limit acceleration value as the boundary parameters of the expected acceleration.

Specifically, after the device obtains the vehicle data and the environment data, it inquires about the corresponding relationship between the vehicle data, 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. Wherein, the vehicle travels within the range between the upper limit acceleration and the lower limit acceleration, which can ensure the safety of the vehicle.

In this embodiment, the user can set the boundary parameters of the vehicle acceleration according to the actual situation, or the device can determine the boundary parameters for the vehicle to be within the safe acceleration according to the vehicle data and the environment data.

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

Step B1, acquiring sensitivity setting parameters.

Specifically, the device may obtain default sensitivity setting parameters, or the user may set the sensitivity parameters when the vehicle is driving automatically on the device, and the device may read the sensitivity parameters set by the user.

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

Among them, the sensitivity setting parameter is inversely proportional to the range of the boundary parameters of the vehicle speed error and the vehicle distance error. The smaller the range of the boundary parameter of the vehicle distance error, the greater the change of the membership degree of the vehicle speed error, and the greater the change of the vehicle speed error. The more sensitive the control is, the same is true for the vehicle distance error.

Specifically, the device can store the corresponding relationship between the sensitivity parameter and the boundary parameters of the vehicle speed error and the vehicle distance error. Boundary parameters of error and vehicle distance error. The device can also pre-store the conversion relationship between the sensitivity parameter and the boundary parameters of the vehicle speed error and the vehicle distance error. Boundary parameter of vehicle distance error.

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

In one embodiment, the above step 101 specifically includes:

Step 101a, obtaining the current vehicle speed of the vehicle, the current vehicle distance between the vehicle and the preceding vehicle, the current vehicle speed of the preceding vehicle, and the expected vehicle distance between the vehicle and the preceding vehicle.

Specifically, the device measures the distance between the current vehicle and the rear of the vehicle in front through the vehicle's integrated camera system, infrared sensor, sonar sensor, millimeter-wave radar and other on-board sensors to obtain the current vehicle distance. The device calculates the speed of the vehicle in front through the change rate of the distance from the vehicle in front and the current speed of the vehicle. The device obtains the current driving speed of the vehicle through the integrated acceleration sensor of the vehicle, or the built-in speedometer, etc., and obtains the current speed of the vehicle. The device obtains the expected distance between the vehicle and the vehicle in front according to the user's input or the calculation of the safe distance.

Step 101b. Determine the difference between the current vehicle speed of the vehicle and the current vehicle speed of the preceding vehicle as the current vehicle speed error, and determine the difference between the current vehicle distance and the expected vehicle distance as the current vehicle distance error.

Specifically, after the device obtains the current speed of the vehicle and the current speed of the vehicle in front, it subtracts the current speed of the vehicle from the current speed of the vehicle in front to obtain the current speed error. After the device obtains the current vehicle distance and the expected vehicle distance, it subtracts the current vehicle distance from the expected vehicle distance to obtain the current vehicle distance error.

Next, a specific embodiment of the present application will be described in detail.

Record the longitudinal distance dt between the vehicles in front, the expected distance between vehicles is ds, define the distance error ex=dt-ds, record the speed of the vehicle in front as v1, and the speed of the own vehicle as v2, define the speed error ev is ev=v2-v1.

The "if-then" conditional proposition that defines fuzzy reasoning is:

"If the vehicle's acceleration _is a, then the inter-vehicle distance error ex and the vehicle speed error e _v will decrease and tend to 0".

Wherein, instead describe what needs to be planned in the if antecedent, that is, the acceleration (equivalent to the vehicle speed), and describe the planning target in the then latter.

The truth function corresponding to the former part "the acceleration of the vehicle is a" is μ _F (a), and the truth function corresponding to the latter part "the inter-vehicle distance error e _x and vehicle speed error e _v will decrease and tend to 0" is f( x), taking the fuzzy implication as the truth value of the "if-then" conditional proposition, and selecting the Lukasiewicz type fuzzy implication, that is, the truth value of the "if-then" conditional proposition is z=I(x,f(x))=1- x+xf(x).

Record the expected acceleration fuzzy set F as the "positive value" for the expected acceleration a, that is, the greater the acceleration a is, the greater the probability of acceleration a's membership degree acceleration fuzzy combination F is, and record the membership function of acceleration a belonging to the acceleration fuzzy set F Denoted as μ _F (a). Note that the vehicle distance error fuzzy set G is the " _{positive size" of the vehicle distance error e x, the greater the probability of the vehicle distance error fuzzy combination G, the vehicle distance error e x belongs to} the membership function of the fuzzy set G _Denote as μ _G (ex ). Note that the vehicle speed error fuzzy set H is the "right size" of the vehicle speed error e _v , the greater the probability of the vehicle speed error e _v 's membership degree and the vehicle speed error fuzzy combination H, the membership function of the vehicle speed error e _v belonging to the fuzzy set H is denoted as μ _H ( e _v ).

Define μ _F (a), μ _G ( _ex ), μ _H (e _v ) as the following three formulas:

Each piece of empirical data is (e _v , e _x , a), and the corresponding three membership functions μ _F (a), μ _G ( _ex ), μ _H (e _v ), and each piece of empirical function corresponds to Membership degree data (μ _F (a), μ _G ( _ex ), μ _H ( _ev )), after that, multi-group (μ _F (a), μ _G ( _ex ), μ _H ( e _v )) for interpolation, the continuous transmission function after interpolation:

μ _F (a) _＝ h(μ _G (ex ), μ _H (e _v )), the form of function h(·,·) is:

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

The truth function of the conditional proposition then is defined as:

Then the truth value of the conditional proposition z=I(x,f(x))=1-x+xf(x), we can get:

When _x =h(μ _G (ex ), μ _H (e _v )),

Design a controller with Lyapunov stability:

u(t) controls the rate of change of x(t), so that the truth value of the fuzzy reasoning process based on the "if-then" conditional proposition is non-increasing, that is, the control of the true value z is non-increasing, and the fuzzy reasoning process is controlled to reach a local minimum at the true value z value to avoid situations where the truth value increases as a result of continuing reasoning (at a local minimum, continuing reasoning would cause the truth value to reach a point larger than the local minimum).

Specifically, make

u(t)=h(μ _G (e _x ), μ _H (e _v ))-x

It can be proved that the control u(t) can control z to z _min , that is, the whole change process of z is non-increasing. The specific proof needs to perform a state transformation on z, and map the bounded z to the unbounded w, namely:

in,

The convergence of z to z _min is equivalent to the convergence of w to 0. By proving the Lyapunov stability of w=0 is equivalent to proving the stability at z=z _min , it is also proved that the reasoning truth value is non-increasing Basic scientific basis.

Differentiate with respect to time to get:

Choose the Lyapunov function as:

V(w)＝e ^w -w-1

Differentiate with respect to time to get:

available after sorting

并且

当且仅当x＝h(μ_G(e_x)，μ_H(e_v))，也就是z＝z_min时发生。因此证明了

的负定性，也即z＝z_min处的稳定性。控制u(t)最终实现的效果是将x控制到了h(μ_G(e_x)，μ_H(e_v))处，通过李雅普诺夫稳定性保证满足了推理过程真值非增的基本科学依据。therefore,

and

Occurs if and only if _x = h(μ _G (ex ), μ _H ( _ev )), ie z = z _min . thus proving

The negative characterization of , that is, the stability at z=z _min . The final effect of controlling u(t) is to control _x to h(μ _G (ex ), μ _H ( _ev )), and the basic science of non-increasing truth value in the reasoning process is satisfied through the Lyapunov stability guarantee in accordance with.

It should be understood that although the steps in the flow charts involved in the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flow charts involved in the above-mentioned embodiments may include multiple steps or stages, and these steps or stages are not necessarily executed at the same time, but may be performed at different times For execution, the execution order of these steps or stages is not necessarily performed sequentially, but may be executed in turn or alternately with other steps or at least a part of steps or stages in other steps.

Based on the same inventive concept, the embodiment of the present application also provides a vehicle distance keeping based on fuzzy reasoning truth evolution based on the fuzzy reasoning truth evolution based vehicle distance keeping speed planning method mentioned above. Speed planning device. The solution to the problem provided by the device is similar to the implementation described in the above method, so the specific limitations in one or more embodiments of the vehicle distance keeping speed planning device based on fuzzy reasoning truth evolution provided below can be See above for the definition of a speed planning method based on fuzzy inference truth-value evolution for keeping distance between vehicles, and details will not be repeated here.

In one embodiment, as shown in FIG. 2 , a vehicle distance keeping speed planning device based on fuzzy inference truth evolution is provided, including:

An acquisition module 201, configured to acquire the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed;

The membership degree determination module 203 is used to determine the vehicle distance error membership degree corresponding to the current vehicle distance error according to the membership degree function corresponding to the vehicle distance error; and determine the vehicle speed error corresponding to the membership degree function according to the vehicle speed error. Speed error membership degree;

The acceleration determination module 205 is configured to determine the value of the inference loss function according to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration, and the inference loss function The acceleration at the minimum value is used as the expected acceleration; the membership experience function is used to reflect the relationship between 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 to reflect the degree of loss of inference credibility from the expected acceleration inference to the vehicle speed error and the vehicle distance error.

In one embodiment, the above-mentioned device also includes:

The experience data acquiring module 209 (not shown in the figure), is used for acquiring the historical driving experience data of the vehicle; the historical experience data includes acceleration, vehicle distance error, and vehicle speed error;

The membership degree acquisition module 211 (not shown in the figure) is used to determine the membership degree function corresponding to the vehicle distance error, the membership degree function corresponding to the vehicle speed error, and the acceleration Describe the historical acceleration membership degree, historical vehicle distance error membership degree, and historical vehicle speed error membership degree of acceleration, vehicle distance error, and vehicle speed error in the historical driving experience data;

Empirical function construction function 213 (not shown in the figure), which is used to obtain and reflect the desired acceleration 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. The membership degree experience function of the relationship among the membership degree of the vehicle speed error and the membership degree of the vehicle distance error.

In one embodiment, the above-mentioned device also includes:

Boundary parameter acquisition module 215 (not shown in the figure), is used for respectively acquiring the boundary parameters of expected acceleration, vehicle speed error, and vehicle distance error;

Vehicle speed error membership construction function 217 (not shown in the figure), used to construct a function whose boundary parameters meet the boundary parameters corresponding to the vehicle speed error, as the membership function of the vehicle speed error;

The vehicle distance error membership construction function 219 (not shown in the figure), constructs a function whose boundary parameters meet the boundary parameters corresponding to the vehicle distance error, as the membership function of the vehicle distance error;

The acceleration membership construction function 221 (not shown in the figure) constructs a function whose boundary parameters meet the boundary parameters corresponding to the expected acceleration as the membership function corresponding to the expected acceleration.

In one embodiment, the above-mentioned boundary parameter acquisition module 215 is specifically used for:

Acquiring vehicle data and environmental data; determining an upper limit acceleration value and a lower limit acceleration value corresponding to the expected acceleration according to the vehicle data and the environmental data, and using the upper limit acceleration value and the lower limit acceleration value as the expected acceleration Boundary parameters.

In one embodiment, the above-mentioned boundary parameter acquisition module 215 is specifically used for:

Acquire sensitivity setting parameters; determine the boundary parameters of the vehicle speed error and the vehicle distance error according to the sensitivity setting parameters; the sensitivity setting parameters are inversely proportional to the range of the vehicle speed error and the boundary parameters of the vehicle distance error .

In one embodiment, the acquisition module 201 is specifically used for:

Obtain the current speed of the vehicle, the current distance between the vehicle and the vehicle in front, the current speed of the vehicle in front, and the expected distance between the vehicle and the vehicle in front; The difference between the vehicle speed and the current vehicle speed of the preceding vehicle is determined as the current vehicle speed error, and the difference between the current vehicle distance and the expected vehicle distance is determined as the current vehicle distance error.

Each module in the above-mentioned vehicle distance keeping speed planning device based on fuzzy inference truth-value evolution can be fully or partially realized by software, hardware and combinations thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute the corresponding operations of the above-mentioned modules.

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

Those skilled in the art can understand that the structure shown in Figure 3 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation to the computer equipment on which the solution of the application is applied. The specific computer equipment can be More or fewer components than shown in the figures may be included, or some components may be combined, or have a different arrangement of components.

In one embodiment, there is also provided a computer device, including a memory and a processor, where a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all Information and data authorized by the user or fully authorized by all parties.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware, and the computer programs can be stored in a non-volatile computer-readable memory In the medium, when the computer program is executed, it may include the processes of the embodiments of the above-mentioned methods. Wherein, any reference to storage, database or other media used in the various embodiments provided in the present application may include at least one of non-volatile and volatile storage. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive variable memory (ReRAM), magnetic variable memory (Magnetoresistive Random Access Memory, MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (Phase Change Memory, PCM), graphene memory, etc. The volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory. As an illustration and not a limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a blockchain-based distributed database, etc., but is not limited thereto. The processors involved in the various embodiments provided by this application can be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., and are not limited to this.

The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be It is considered to be within the range described in this specification.

The above-mentioned embodiments only express several implementation modes of the present application, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be determined by the appended claims.

Claims (10)

1. A distance between vehicles based on fuzzy reasoning truth-value evolution keeps speed planning method, it is characterized in that, described method comprises: Obtain the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed; According to the membership degree function corresponding to the vehicle distance error, determine the vehicle distance error membership degree corresponding to the current vehicle distance error; according to the vehicle speed error corresponding membership degree function, determine the vehicle speed error membership degree corresponding to the current vehicle speed error; According to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the empirical function of the membership degree, the membership degree function corresponding to the acceleration and the inference loss function, determine the acceleration that makes the value of the inference loss function at a minimum value, as The expected acceleration of the vehicle; the membership degree empirical function is used to reflect the relationship between 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 reasoning loss function is used for Reflecting the degree of loss of inference reliability from the expected acceleration to the vehicle speed error and the vehicle distance error. 2. The method according to claim 1, wherein, according to the membership degree of the vehicle distance error, the membership of the vehicle speed error, the membership experience function, the membership function corresponding to the acceleration and the inference loss function, determine Before making the value of the inference loss function at the minimum acceleration, as the expected acceleration of the vehicle, the method also includes: Acquiring historical driving experience data of the vehicle; the historical experience data includes acceleration, vehicle distance error, and vehicle speed error; According to the membership degree function corresponding to the vehicle distance error, the membership degree function corresponding to the vehicle speed error, and the membership degree function corresponding to the acceleration, respectively determine the acceleration, vehicle distance error, and vehicle speed error in the historical driving experience data. The membership degree of historical acceleration, the membership degree of historical vehicle distance error, and the membership degree of historical vehicle speed 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, the membership degree for reflecting the expected acceleration, the membership degree of the vehicle speed error, and the vehicle speed error membership degree are obtained by fitting. The membership degree empirical function of the relationship between the membership degrees from the error. 3. The method according to claim 1, wherein, according to the corresponding membership function of the vehicle distance error, the vehicle distance error membership degree corresponding to the current vehicle distance error is determined; according to the vehicle speed error corresponding membership degree function, Before determining the vehicle speed error membership degree corresponding to the current vehicle speed error, the method further includes: Obtain the boundary parameters of expected acceleration, vehicle speed error, and vehicle distance error respectively; Constructing a function whose boundary parameter conforms to the boundary parameter corresponding to the vehicle speed error, as the membership function of the vehicle speed error; Constructing a function whose boundary parameter conforms to the boundary parameter corresponding to the vehicle distance error, as the membership function of the vehicle distance error; A function whose boundary parameters conform to the boundary parameters corresponding to the expected acceleration is constructed as a membership function corresponding to the expected acceleration. 4. The method according to claim 3, wherein said obtaining the boundary parameters of expected acceleration comprises: Obtain vehicle data and environmental data; According to the vehicle data and the environment data, an upper limit acceleration value and a lower limit acceleration value corresponding to the expected acceleration are determined, and the upper limit acceleration value and the lower limit acceleration value are used as boundary parameters of the expected acceleration. 5. method according to claim 3, is characterized in that, described obtaining the boundary parameter of vehicle speed error, vehicle distance error, comprises: Get the sensitivity setting parameters; Determine the boundary parameters of the vehicle speed error and the vehicle distance error according to the sensitivity setting parameters; the sensitivity setting parameters are inversely proportional to the ranges of the vehicle speed error and the boundary parameters of the vehicle distance error. 6. The method according to claim 1, wherein said obtaining the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed comprises: Obtaining the current vehicle speed of the vehicle, the current vehicle distance between the vehicle and the preceding vehicle, the current vehicle speed of the preceding vehicle, and the expected vehicle distance between the vehicle and the preceding vehicle; The difference between the current vehicle speed of the vehicle and the current vehicle speed of the preceding vehicle is determined as a current vehicle speed error, and the difference between the current vehicle distance and the expected vehicle distance is determined as a current vehicle distance error. 7. A vehicle distance keeping speed planning device based on fuzzy reasoning truth evolution, characterized in that said device comprises: An acquisition module, configured to acquire the current vehicle distance error between the current vehicle distance and the expected vehicle distance, and the current vehicle speed error between the current vehicle speed and the expected vehicle speed; The membership determination module is used to determine the vehicle distance error membership corresponding to the current vehicle distance error according to the membership function corresponding to the vehicle distance error; and determine the vehicle speed corresponding to the current vehicle speed error according to the membership function corresponding to the vehicle speed error Error membership degree; The acceleration determination module is used to determine the value of the inference loss function to be in The acceleration of the minimum value is used as the expected acceleration of the vehicle; the membership empirical function is used to reflect the relationship between the membership of the desired acceleration, the membership of the vehicle speed error, and the membership of the vehicle distance error; The inference loss function is used to reflect the degree of loss of inference credibility from the expected acceleration inference to the vehicle speed error and the vehicle distance error. 8. A computer device, comprising a memory and a processor, the memory stores a computer program, wherein the processor implements the method according to any one of claims 1 to 6 when executing the computer program step. 9. A computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are realized. 10. A computer program product, comprising a computer program, characterized in that, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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