Self-adaptive cruise control method and system and vehicle

Abstract

The embodiment of the invention provides a self-adaptive cruise control method and system and a vehicle, and the method comprises the steps: determining a workshop time interval value and a collision risk degree value of two workshops according to a driving state; according to the two values, determining a target fuzzy subset corresponding to the workshop time interval value in the workshop time interval fuzzy set and a target fuzzy subset corresponding to the risk degree value in the risk degree fuzzy set; according to a fuzzy rule, determining a first fuzzy subset corresponding to the target fuzzy subset in a first fuzzy set of a first correction variable, the first correction variable being a correction variable corresponding to the first index weight; performing defuzzification on the first fuzzy subset to obtain a corresponding target value; correcting the first index weight through the target value to obtain a first corrected index weight; and performing adaptive cruise control based on the first correction index weight. The adaptive cruise control method aims at improving the adaptability of adaptive cruise control so as to improve the comfort and reliability of driving.

Description

Technical Field

[0001] The present application relates to the technical field of cruise control, and in particular to an adaptive cruise control method, system and vehicle. Background Art

[0002] The CACC system (Cooperative Adaptive Cruise Control) is a complex control system involving multiple fields. It is necessary to fully consider the relationship between people, vehicles and roads to achieve effective vehicle following control and traffic management. When the traditional upper-level MPC controller in the CACC system is operating, the weight distribution of the safety, vehicle following and comfort of the CACC system is fixed, which cannot adapt well to complex road conditions and will reduce driving comfort and reliability. Summary of the invention

[0003] In view of this, the embodiments of the present application provide an adaptive cruise control method, system and vehicle, aiming to improve the adaptability of the adaptive cruise control, so as to improve the driving comfort and reliability.

[0004] A first aspect of the present application provides an adaptive cruise control method, the method comprising:

[0005] According to the driving status of the vehicles, the time distance between the two vehicles and the risk level of collision are determined;

[0006] According to the workshop time distance value and the risk level value, determine a target fuzzy subset corresponding to the workshop time distance value in a workshop time distance fuzzy set and a target fuzzy subset corresponding to the risk level value in a risk level fuzzy set;

[0007] According to the fuzzy rule, determining a first fuzzy subset corresponding to the target fuzzy subset in a first fuzzy set corresponding to a first correction variable, wherein the first correction variable is a correction variable corresponding to a first indicator weight, and the first indicator weight is a safety indicator weight or a comfort indicator weight;

[0008] Defuzzifying the first fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the first correction variable;

[0009] Correcting the first indicator weight by using the target value corresponding to the first correction variable to obtain a first corrected indicator weight;

[0010] Adaptive cruise control is performed based on the first correction index weight.

[0011] Optionally, before performing adaptive cruise control based on the first correction index weight, the method further includes:

[0012] According to the fuzzy rule, a second fuzzy subset corresponding to the target fuzzy subset in a second fuzzy set corresponding to a second correction variable is determined, wherein the second correction variable is a correction variable corresponding to a second index weight, and the second index weight is an index weight different from the first index weight among a safety index weight and a comfort index weight;

[0013] Defuzzifying the second fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the second correction variable;

[0014] The second indicator weight is corrected by the target value corresponding to the second correction variable to obtain a second corrected indicator weight;

[0015] The performing adaptive cruise control based on the first correction index weight includes: performing adaptive cruise control based on the first correction index weight and the second correction index weight.

[0016] Optionally, according to the workshop time distance value and the risk level value, determining a target fuzzy subset corresponding to the workshop time distance value in the workshop time distance fuzzy set and a target fuzzy subset corresponding to the risk level value in the risk level fuzzy set includes:

[0017] According to the time headway value, the membership value under the time headway value is calculated respectively by using the membership function of each fuzzy subset in the time headway fuzzy set, and the fuzzy subset corresponding to the maximum membership value is determined as the target fuzzy subset corresponding to the time headway value;

[0018] According to the risk level value, the membership value under the risk level value is calculated respectively by the membership function of each fuzzy subset in the risk level fuzzy set, and the fuzzy subset corresponding to the maximum membership value is determined as the target fuzzy subset corresponding to the risk level value.

[0019] Optionally, determining, according to a fuzzy rule, a first fuzzy subset in a first fuzzy set corresponding to the first correction variable and corresponding to the target fuzzy subset includes:

[0020] According to fuzzy rules, a fuzzy subset corresponding to the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value in the first fuzzy set corresponding to the first correction variable is determined, wherein the fuzzy rule defines a corresponding relationship between the fuzzy subsets in the first fuzzy set and the fuzzy subsets in the time headway fuzzy set and the fuzzy subsets in the risk level fuzzy set;

[0021] The fuzzy subset corresponding to both the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value in the determined first fuzzy set is determined as the first fuzzy subset corresponding to the first correction variable.

[0022] Optionally, determining, according to a fuzzy rule, a second fuzzy subset in a second fuzzy set corresponding to the second correction variable and corresponding to the target fuzzy subset includes:

[0023] Determine, according to a fuzzy rule, a fuzzy subset in the second fuzzy set corresponding to the second correction variable that corresponds to both the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value, wherein the fuzzy rule defines a corresponding relationship between the fuzzy subset in the second fuzzy set and both the fuzzy subset in the time headway fuzzy set and the fuzzy subset in the risk level fuzzy set;

[0024] The fuzzy subset corresponding to both the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value in the determined second fuzzy set is determined as the second fuzzy subset corresponding to the second correction variable.

[0025] Optionally, when the first fuzzy subset includes a plurality of fuzzy subsets, before defuzzifying the first fuzzy subset by a defuzzification algorithm to obtain a target value corresponding to the first correction variable, the method further includes:

[0026] According to the membership value of the target fuzzy subset corresponding to the first fuzzy subset, the minimum membership value is determined as the activation degree of the first fuzzy subset;

[0027] According to the activation degree of each first fuzzy subset, all the first fuzzy subsets are weighted averaged to obtain the final first fuzzy subset;

[0028] In the case where the second fuzzy subset includes a plurality of sets, before defuzzifying the second fuzzy subset by a defuzzification algorithm to obtain a target value corresponding to the second correction variable, the method further includes:

[0029] According to the membership value of the target fuzzy subset corresponding to the second fuzzy subset, the minimum membership value is determined as the activation degree of the second fuzzy subset;

[0030] According to the activation degree of each second fuzzy subset, all the second fuzzy subsets are weighted averaged to obtain the final second fuzzy subset.

[0031] Optionally, before performing adaptive cruise control based on the first correction index weight and the second correction index weight, the method further includes:

[0032] The vehicle distance is determined by integrating the spacing strategy, and the integration spacing strategy expression is: Among them, d exp Indicates the required workshop distance; d0 is the minimum safe distance between workshops; d c is the correction term; t h and c are fixed constant coefficients; v is the vehicle speed; v head is the speed of the pilot vehicle; v rel_head is the speed difference between the two vehicles;

[0033] The performing of adaptive cruise control based on the first correction index weight and the second correction index weight includes: performing adaptive cruise control based on the first correction index weight, the second correction index weight and the required vehicle-to-vehicle distance to be maintained.

[0034] Optionally, according to the driving status of the vehicles, the time distance between the two vehicles and the risk level of collision are determined, including:

[0035] According to the vehicle driving status, the time distance between two vehicles is calculated by the time distance determination algorithm, and the time distance between two vehicles is obtained. The expression of the time distance determination algorithm is: Among them, T thw represents the time interval between two vehicles, d represents the distance between two vehicles, and d c is the correction term, v is the speed of the vehicle;

[0036] According to the driving status of the vehicles, the risk level determination algorithm is used to calculate and obtain the risk level of collision between the two vehicles. The risk level determination algorithm expression is: Among them, T ttc -1 Indicates the risk level, v rel represents the speed difference between the two vehicles, d represents the distance between the two vehicles, and v p Indicates the speed of the vehicle ahead.

[0037] A second aspect of the present application provides an adaptive cruise control system, the system comprising:

[0038] A parameter determination module, used to determine the time interval between two vehicles and the risk level of collision according to the driving status of the vehicles;

[0039] A target fuzzy subset determination module is used to determine, according to the workshop time distance value and the risk level value, a target fuzzy subset corresponding to the workshop time distance value in the workshop time distance fuzzy set and a target fuzzy subset corresponding to the risk level value in the risk level fuzzy set;

[0040] A first fuzzy subset determination module is used to determine, according to a fuzzy rule, a first fuzzy subset corresponding to the target fuzzy subset in a first fuzzy set corresponding to a first correction variable, wherein the first correction variable is a correction variable corresponding to a first indicator weight, and the first indicator weight is a safety indicator weight or a comfort indicator weight;

[0041] a defuzzification processing module, configured to defuzzify the first fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the first correction variable;

[0042] A correction module, used for correcting the first indicator weight according to the target value corresponding to the first correction variable to obtain a first corrected indicator weight;

[0043] A cruise control module is configured to perform adaptive cruise control based on the first correction index weight.

[0044] A third aspect of the present application provides a vehicle, wherein the vehicle is provided with an adaptive cruise control system provided by the second aspect of the present application, and is used to execute the steps in an adaptive cruise control method provided by the first aspect of the present application.

[0045] The adaptive cruise control method provided by the present application has the following advantages:

[0046] An adaptive cruise control method provided in an embodiment of the present application introduces fuzzy theory to optimize the MPC algorithm (Model Predictive Control) in the CACC system. First, according to the driving state of the vehicle, the time interval between the two vehicles and the risk value of a collision are determined; at the same time, a fuzzy set of time intervals corresponding to the time interval between the two vehicles is pre-established, and the fuzzy set of time intervals fuzzy sets includes multiple fuzzy subsets. At the same time, a fuzzy set of risk levels corresponding to the risk levels of a collision is pre-established, and the fuzzy set of risk levels fuzzy sets includes multiple fuzzy subsets; according to the determined time intervals and risk values, a target fuzzy subset corresponding to the time interval value in the fuzzy set of time intervals is determined, and a target fuzzy subset corresponding to the risk value in the fuzzy set of risk levels is determined; at the same time, a first fuzzy set corresponding to a first correction variable is pre-established, and the first fuzzy set includes multiple fuzzy subsets; according to the model Fuzzy rules, determine a fuzzy subset corresponding to the target fuzzy subset corresponding to the time interval between the vehicles and the target fuzzy subset corresponding to the risk level in the first fuzzy set corresponding to the first correction variable, determine the fuzzy subset as the first fuzzy subset corresponding to the first correction variable, the first correction variable is the correction variable corresponding to the first indicator weight, the first indicator weight is the safety indicator weight or the comfort indicator weight; defuzzify the first fuzzy subset corresponding to the first correction variable through the defuzzification algorithm to obtain the target value corresponding to the first correction variable; then correct the first indicator weight through the target value corresponding to the first correction variable to obtain the first correction indicator weight; finally, the CACC system performs adaptive cruise control based on the first correction indicator weight. Therefore, the present application introduces fuzzy theory to adjust the weight distribution of various performance indicators from the previous fixed to dynamic adjustment based on the actual driving state of the vehicle, so as to effectively improve the adaptability of adaptive cruise control and improve driving comfort and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 A system structure diagram of a CACC system in an adaptive cruise control method shown in one embodiment of the present application;

[0049] Figure 2 A flowchart of an adaptive cruise control method shown in one embodiment of the present application;

[0050] Figure 3 A flowchart of fuzzy control in an adaptive cruise control method according to an embodiment of the present application;

[0051] Figure 4 A flowchart of an adaptive cruise control method in an adaptive cruise control method according to an embodiment of the present application;

[0052] Figure 5 A schematic diagram of an adaptive cruise control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] Before explaining this application, the current background is explained first. The CACC system (Cooperative Adaptive Cruise Control) is a complex control system involving multiple fields. It is necessary to fully consider the relationship between people, vehicles and roads in order to achieve effective vehicle following control and traffic management. Figure 1 As shown, Figure 1The schematic diagram of the CACC system structure of an electric vehicle in a V2X environment is shown. The main components of the CACC system and the functions of each component are as follows: HMI human-machine interaction interface, which provides a platform for the driver to interact with the CACC system, including displaying the current vehicle speed, following vehicle distance and other information, and adjusting the CACC system parameters; driver priority control strategy, which allows the driver to control the vehicle speed and acceleration by stepping on the brake or accelerator pedal at any time to ensure the driver's control over the vehicle; cruise control / following mode switching strategy, which is used to perform logical judgment to automatically adjust the vehicle driving mode and send a mode signal to the upper controller; following vehicle distance strategy, which is used to transmit the expected following vehicle distance to the upper controller according to the vehicle status information, and control the acceleration and braking force of the vehicle, so as to ensure the driver's control over the vehicle. Maintain a safe distance from the vehicle in front; The lower controller is used to reverse analyze the vehicle dynamics, obtain the control method of the drive and brake systems, and combine the brake / drive switching control strategy to convert the expected acceleration signal given by the upper layer into the expected driving torque and brake pressure signal that the actuator can accept, so as to complete the longitudinal motion control of the vehicle; The upper controller (core) is used to solve the most appropriate expected acceleration according to the vehicle information and driving mode, meet the driver's driving style needs while ensuring vehicle safety, and optimize the comfort of passengers and the followability of the queue as much as possible, and transmit the corresponding signal to the lower controller; The vehicle dynamics model group is used to describe the dynamic characteristics of the vehicle during movement, including acceleration, braking force, turning radius and other parameters, which is an important part of the CACC system. For the MPC algorithm in the upper controller of the CACC system, its implementation mainly consists of three basic steps: prediction model, rolling optimization, and feedback correction. The most core step is the establishment of the prediction model, in which the coefficient ω of the index weight exists in the comprehensive performance function equation output at the end. y , whose matrix is where ω x =diag[ω safe ω vrel ω comf ],N p is the prediction time domain, ω safe is the safety index weight, ω vrel is the weight of the following index, ω comf is the comfort index weight. When the traditional upper controller is in operation, the weights of the three performance indicators of the CACC system are safe ,ω vrel ,ω comfThe weight distribution is fixed (generally set to a fixed value in the early stage), which cannot adapt well to complex road conditions and will reduce driving comfort and reliability. The present application mainly optimizes the upper-level control of the MPC algorithm in the CACC system, specifically dynamically adjusts the performance indicator weights based on the vehicle's driving status, thereby effectively improving the adaptability of adaptive cruise control and improving driving comfort and reliability. In addition, the present application also provides other related implementation methods to optimize the following vehicle spacing strategy in the CACC system, so that the vehicle can maintain the safety of following the vehicle and the comfort of speed changes under different conditions.

[0055] refer to Figure 2 , Figure 2 This is a schematic diagram of an adaptive cruise control method according to an embodiment of the present application. Figure 2 As shown, the present application provides an adaptive cruise control method, comprising:

[0056] Step S1: Determine the time distance between two vehicles and the risk level of collision according to the driving status of the vehicles.

[0057] In this embodiment, first, according to the driving state of the vehicle, the time distance between the vehicle and the front vehicle and the risk level of a collision between the vehicle and the front vehicle are determined. The time distance reflects the time required for the vehicle to reach the position of the front vehicle at the current moment, and the risk level of a collision reflects whether the vehicle is approaching the front vehicle, that is, the possibility of a collision.

[0058] Step S2: According to the workshop time distance value and the risk level value, determine the target fuzzy subset corresponding to the workshop time distance value in the workshop time distance fuzzy set and the target fuzzy subset corresponding to the risk level value in the risk level fuzzy set.

[0059] In this embodiment, the present application introduces fuzzy theory, and conducts a large number of experimental calibrations in advance based on prior expert knowledge for the workshop time interval parameters and risk level parameters, constructs a workshop time interval fuzzy set corresponding to the workshop time interval parameters, and the workshop time interval fuzzy set includes multiple fuzzy subsets. At the same time, a risk level fuzzy set corresponding to the risk level parameters is constructed, and the risk level fuzzy set includes multiple fuzzy subsets.

[0060] In this embodiment, after the current headway value and risk level value between the vehicle and the preceding vehicle are obtained by calculation in step S1, the headway value is calculated by a relevant algorithm to determine the fuzzy subset corresponding to the headway value in the headway fuzzy set, and the fuzzy subset is determined as the target fuzzy subset corresponding to the headway value; and the risk level value is calculated by a relevant algorithm to determine the fuzzy subset corresponding to the risk level value in the risk level fuzzy set, and the fuzzy subset is determined as the target fuzzy subset corresponding to the risk level value.

[0061] Step S3: According to fuzzy rules, determine the first fuzzy subset corresponding to the target fuzzy subset in the first fuzzy set corresponding to the first correction variable, the first correction variable is the correction variable corresponding to the first indicator weight, and the first indicator weight is the safety indicator weight or the comfort indicator weight.

[0062] In the present application, step S3 may specifically include: determining, according to fuzzy rules, a fuzzy subset in the first fuzzy set corresponding to the first correction variable that is simultaneously corresponding to the target fuzzy subset corresponding to the time interval between the workshops and the value of the workshop and the target fuzzy subset corresponding to the risk level value, wherein the fuzzy rule defines the correspondence between the fuzzy subsets in the first fuzzy set and the fuzzy subsets in the time interval between the workshops and the fuzzy subsets in the risk level fuzzy set; determining the fuzzy subset in the determined first fuzzy set that is simultaneously corresponding to the target fuzzy subset corresponding to the time interval between the workshops and the value of the workshop and the target fuzzy subset corresponding to the risk level value as the first fuzzy subset corresponding to the first correction variable.

[0063] In this embodiment, a large number of experimental calibrations are performed in advance based on prior expert knowledge for the first correction variable to construct a first fuzzy set corresponding to the first correction variable, and the first fuzzy set includes multiple fuzzy subsets. At the same time, fuzzy rules are pre-set, and the fuzzy rules at least define the corresponding relationship between each fuzzy subset in the first fuzzy set of the first correction variable and each fuzzy subset in the fuzzy set of the headway time and the fuzzy set of the risk level.

[0064] For example, assume that the pre-established workshop time fuzzy set T thw ={NB thw ,NS thw ,ZO thw ,PS thw ,PB thw} includes five fuzzy subsets, namely NB thw , NS thw , ZO thw , P.S. thw , P.B. thw ; Pre-established risk level fuzzy set T ttc-1 ={NB ttc ,NS ttc ,ZO ttc ,PS ttc ,PB ttc} includes 5 fuzzy subsets, namely NB ttc , NS ttc , ZO ttc , P.S. ttc , P.B. ttc ; The first fuzzy set C1={NB1,NS1,ZO1,PS1,PB1} corresponding to the first correction variable established in advance includes 5 fuzzy subsets, namely NB1, NS1, ZO1, PS1, PB1, and it is assumed that the first index weight corresponding to the first correction variable is the comfort index weight. Based on the pre-established headway fuzzy set, risk level fuzzy set and first fuzzy set, the corresponding fuzzy rules are established. The fuzzy rules define the corresponding relationship between each fuzzy subset in the first fuzzy set of the first correction variable and each fuzzy subset in the headway fuzzy set and risk level fuzzy set, as shown in Table 1. The corresponding relationship recorded in Table 1 is the established fuzzy rules, where NB represents negative large, i.e. shrinking; NS represents negative small, i.e. slightly shrinking; ZO is zero, i.e. no correction is made; PS represents positive small, i.e. slightly expanding; PB represents positive large, i.e. expanding.

[0065] Table 1

[0066]

[0067] In this embodiment, based on the fuzzy rule, a fuzzy subset in the first fuzzy set corresponding to the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value determined in step S2 is determined, and then the fuzzy subset is determined as the first fuzzy subset corresponding to the first correction variable. For example, continuing with the above example, when the target fuzzy subset corresponding to the time headway value is determined to be NS thw And the target fuzzy subset corresponding to the risk level is ZO ttc , through the fuzzy rules shown in Table 1, determine the simultaneous thw and ZO ttc The corresponding fuzzy subset in the first fuzzy set is NS1, so the NS1 fuzzy subset in the first fuzzy set is determined to be the first fuzzy subset corresponding to the first correction variable. Among them, the first correction variable is a correction variable corresponding to the first index weight, and the first index weight can be a safety index weight or a comfort index weight. In the case where the first index weight is a safety index weight, the present application dynamically adjusts the safety index weight, and in the case where the first index weight is a comfort index weight, the present application dynamically adjusts the comfort index weight.

[0068] Step S4: Defuzzify the first fuzzy subset using a defuzzification algorithm to obtain a target value corresponding to the first correction variable.

[0069] In this embodiment, after the first fuzzy subset corresponding to the first correction variable is obtained in step S3, the first fuzzy subset is defuzzified by a defuzzification algorithm to obtain a determined target value corresponding to the first correction variable. The defuzzification algorithm is preferably a centroid defuzzification algorithm, and it should be understood that this is only a preferred defuzzification algorithm, and the defuzzification algorithm can also be other defuzzification algorithms, which are not specifically limited here.

[0070] Step S5: Correct the first indicator weight according to the target value corresponding to the first correction variable to obtain a first corrected indicator weight.

[0071] In this embodiment, after the defuzzification processing of step S4, a clear output value (i.e., target value) corresponding to the first correction variable can be obtained, and then the first indicator weight is corrected by the target value to obtain a corrected first indicator weight, which is the first corrected indicator weight.

[0072] Step S6: performing adaptive cruise control based on the first correction index weight.

[0073] In this embodiment, after obtaining the first correction index weight corrected based on the vehicle's driving state through step S5, the CACC system will perform collaborative adaptive cruise control on the vehicle based on the first correction index weight. Compared with the previous collaborative adaptive cruise control of the vehicle with a fixed first index weight, the present application dynamically adjusts the first index weight based on the vehicle's driving state to improve the adaptability of the collaborative adaptive cruise control, thereby achieving the purpose of improving driving comfort and reliability.

[0074] An adaptive cruise control method provided in an embodiment of the present application introduces fuzzy theory to optimize the MPC algorithm (Model Predictive Control) in the CACC system. First, according to the driving state of the vehicle, the time interval between the two vehicles and the risk value of a collision are determined; at the same time, a fuzzy set of time intervals corresponding to the time interval between the two vehicles is pre-established, and the fuzzy set of time intervals fuzzy sets includes multiple fuzzy subsets. At the same time, a fuzzy set of risk levels corresponding to the risk levels of a collision is pre-established, and the fuzzy set of risk levels fuzzy sets includes multiple fuzzy subsets; according to the determined time intervals and risk values, a target fuzzy subset corresponding to the time interval value in the fuzzy set of time intervals is determined, and a target fuzzy subset corresponding to the risk value in the fuzzy set of risk levels is determined; at the same time, a first fuzzy set corresponding to a first correction variable is pre-established, and the first fuzzy set includes multiple fuzzy subsets; according to the model Fuzzy rules, determine a fuzzy subset corresponding to the target fuzzy subset corresponding to the time interval between the vehicles and the target fuzzy subset corresponding to the risk level in the first fuzzy set corresponding to the first correction variable, determine the fuzzy subset as the first fuzzy subset corresponding to the first correction variable, the first correction variable is the correction variable corresponding to the first indicator weight, the first indicator weight is the safety indicator weight or the comfort indicator weight; defuzzify the first fuzzy subset corresponding to the first correction variable through the defuzzification algorithm to obtain the target value corresponding to the first correction variable; then correct the first indicator weight through the target value corresponding to the first correction variable to obtain the first correction indicator weight; finally, the CACC system performs adaptive cruise control based on the first correction indicator weight. Therefore, the present application introduces fuzzy theory to adjust the weight distribution of various performance indicators from the previous fixed to dynamic adjustment based on the actual driving state of the vehicle, so as to effectively improve the adaptability of adaptive cruise control and improve driving comfort and reliability.

[0075] In combination with the above embodiments, in one implementation, the present application embodiment further provides an adaptive cruise control method. In the adaptive cruise control method, before step S6, the method further includes steps S01 to S03:

[0076] Step S01: According to fuzzy rules, determine the second fuzzy subset corresponding to the target fuzzy subset in the second fuzzy set corresponding to the second correction variable, the second correction variable is the correction variable corresponding to the second indicator weight, and the second indicator weight is the indicator weight of the safety indicator weight and the comfort indicator weight that is different from the first indicator weight.

[0077] In the present application, step S01 may specifically include: determining, according to fuzzy rules, a fuzzy subset in the second fuzzy set corresponding to the second correction variable that is simultaneously corresponding to the target fuzzy subset corresponding to the time interval between the workshops and the value of the workshop and the target fuzzy subset corresponding to the risk level value, wherein the fuzzy rule defines the correspondence between the fuzzy subsets in the second fuzzy set that are simultaneously corresponding to the fuzzy subsets in the time interval between the workshops and the fuzzy subsets in the risk level fuzzy set; determining the fuzzy subset in the determined second fuzzy set that is simultaneously corresponding to the target fuzzy subset corresponding to the time interval between the workshops and the value of the workshop and the target fuzzy subset corresponding to the risk level value as the second fuzzy subset corresponding to the second correction variable.

[0078] In this embodiment, the previous implementation of the present application only dynamically adjusts one of the safety index weight and the comfort index weight. In order to improve driving comfort and reliability during adaptive cruising, another implementation of the present application will dynamically adjust both the safety index weight and the comfort index weight.

[0079] Specifically, for the second correction variable, a large number of experimental calibrations are performed in advance through prior expert knowledge to construct a second fuzzy set corresponding to the second correction variable, and the second fuzzy set includes multiple fuzzy subsets. At the same time, fuzzy rules are pre-set, and the fuzzy rules at least define the corresponding relationship between each fuzzy subset in the second fuzzy set of the second correction variable and each fuzzy subset in the fuzzy set of the vehicle headway and the fuzzy set of the risk level.

[0080] For example, assume that the pre-established workshop time fuzzy set T thw ={NB thw ,NS thw ,ZO thw ,PS thw ,PB thw} includes five fuzzy subsets, namely NB thw , NS thw , ZO thw , P.S. thw , P.B. thw ; Pre-established risk level fuzzy set T ttc -1 ={NB ttc ,NS ttc ,ZO ttc ,PS ttc ,PB ttc} includes 5 fuzzy subsets, namely NB ttc , NS ttc , ZO ttc , P.S. ttc , P.B. ttc; The pre-established second fuzzy set C2={NB2,NS2,ZO2,PS2,PB2} corresponding to the second correction variable includes 5 fuzzy subsets, namely NB2, NS2, ZO2, PS2, PB2, and it is assumed that the second index weight corresponding to the second correction variable is the safety index weight. Based on the pre-established workshop time fuzzy set, risk level fuzzy set and second fuzzy set, a corresponding fuzzy rule is established, which defines the corresponding relationship between each fuzzy subset in the second fuzzy set of the second correction variable and each fuzzy subset in the workshop time fuzzy set and the risk level fuzzy set, as shown in Table 2. The corresponding relationship recorded in Table 2 is the established fuzzy rule.

[0081] Table 2

[0082]

[0083] In this embodiment, based on the fuzzy rule, a fuzzy subset in the second fuzzy set corresponding to the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value determined in step S2 is determined, and then the fuzzy subset is determined as the second fuzzy subset corresponding to the second correction variable. For example, continuing with the above example, when the target fuzzy subset corresponding to the time headway value is determined to be NS thw And the target fuzzy subset corresponding to the risk level is ZO ttc , through the fuzzy rules shown in Table 2, determine the simultaneous thw and ZO ttc The corresponding fuzzy subset in the second fuzzy set is PS2, so the PS2 fuzzy subset in the second fuzzy set is determined to be the second fuzzy subset corresponding to the second correction variable. The second correction variable is a correction variable corresponding to the second index weight, and the second index weight is an index weight different from the first index weight in the safety index weight and the comfort index weight. For example, when the first index weight is the comfort index weight, the second index weight is the safety index weight; when the first index weight is the safety index weight, the second index weight is the comfort index weight.

[0084] Step S02: Defuzzifying the second fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the second correction variable.

[0085] In this embodiment, after the second fuzzy subset corresponding to the second correction variable is obtained in step S01, the second fuzzy subset is defuzzified by a defuzzification algorithm to obtain a determined target value corresponding to the second correction variable. The defuzzification algorithm is preferably a centroid defuzzification algorithm, and it should be understood that this is only a preferred defuzzification algorithm, and the defuzzification algorithm can also be other defuzzification algorithms, which are not specifically limited here.

[0086] Step S03: Correct the second indicator weight by using the target value corresponding to the second correction variable to obtain a second corrected indicator weight.

[0087] In this embodiment, after the defuzzification processing of step S02, a clear output value (i.e., target value) corresponding to the second correction variable can be obtained, and then the second indicator weight is corrected by the target value to obtain a corrected second indicator weight, which is the second corrected indicator weight.

[0088] In the present application, when the adaptive cruise control method further includes steps S01 to S03, step S6 includes: performing adaptive cruise control based on the first correction index weight and the second correction index weight.

[0089] In this embodiment, after obtaining the first correction index weight corrected based on the vehicle's driving state through step S5 and obtaining the second correction index weight corrected based on the vehicle's driving state through step S03, the CACC system will perform collaborative adaptive cruise control on the vehicle based on the first correction index weight and the second correction index weight. Compared with the previous collaborative adaptive cruise control of the vehicle with fixed first index weights and second index weights, the present application dynamically adjusts the first index weight and the second index weight based on the vehicle's driving state to improve the adaptability of the collaborative adaptive cruise control, thereby achieving the purpose of improving driving comfort and reliability.

[0090] In combination with the above embodiments, in one implementation, the present application embodiment further provides an adaptive cruise control method. In the adaptive cruise control method, step S2 may include steps S21 to S22:

[0091] Step S21: According to the time headway value, the membership value under the time headway value is calculated respectively by using the membership function of each fuzzy subset in the time headway fuzzy set, and the fuzzy subset corresponding to the maximum membership value is determined as the target fuzzy subset corresponding to the time headway value.

[0092] In this embodiment, each fuzzy subset in the pre-established workshop time distance fuzzy set has a corresponding membership function. After obtaining the current workshop time distance value through step S1, the workshop time distance value is respectively substituted into the membership function corresponding to each fuzzy subset for calculation, and a membership value is calculated for each membership function. Then, the largest membership value among all the obtained membership values ​​is screened out, and the fuzzy subset to which the membership function corresponding to the largest membership value belongs is determined as the target fuzzy subset corresponding to the workshop time distance value. Among them, the membership function corresponding to each fuzzy subset in the workshop time distance fuzzy set is preferably a Gaussian membership function.

[0093] Step S22: According to the risk level value, the membership values ​​under the risk level value are calculated respectively by the membership functions of the respective fuzzy subsets in the risk level fuzzy set, and the fuzzy subset corresponding to the maximum membership value is determined as the target fuzzy subset corresponding to the risk level value.

[0094] In this embodiment, each fuzzy subset in the pre-established risk level fuzzy set has a corresponding membership function. After the current risk level value is obtained through step S1, the risk level value is respectively brought into the membership function corresponding to each fuzzy subset for calculation, and a membership value is calculated for each membership function. Then, the largest membership value among all the obtained membership values ​​is screened out, and the fuzzy subset to which the membership function corresponding to the largest membership value belongs is determined as the target fuzzy subset corresponding to the risk level value. Among them, the membership function corresponding to each fuzzy subset in the risk level fuzzy set is preferably a Gaussian membership function.

[0095] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides an adaptive cruise control method. In the adaptive cruise control method, when the first fuzzy subset includes multiple, before step S4, the method further includes step S041 and step S042:

[0096] Step S041: according to the membership value of the target fuzzy subset corresponding to the first fuzzy subset, the minimum membership value is determined as the activation degree of the first fuzzy subset.

[0097] In this embodiment, when it is determined based on pre-established fuzzy rules that the first fuzzy subsets corresponding to the target fuzzy subsets corresponding to the workshop time interval values ​​and the target fuzzy subsets corresponding to the risk level values ​​include multiple first fuzzy subsets, in order to improve the optimization effect, the present application performs weighted averaging on the multiple first fuzzy subsets, and then uses the final first fuzzy subset after the weighted average for subsequent defuzzification.

[0098] Specifically: for each determined first fuzzy subset, there are two corresponding target fuzzy subsets, and the two target fuzzy subsets calculate their own membership values ​​when being determined as target fuzzy subsets. In this application, for each first fuzzy subset, the smallest of the two membership values ​​corresponding to the two target fuzzy subsets corresponding to itself is determined as the activation degree corresponding to the first fuzzy subset.

[0099] Step S042: according to the activation degree of each first fuzzy subset, weighted average is performed on all first fuzzy subsets to obtain a final first fuzzy subset.

[0100] In this embodiment, based on the activation degrees of the determined multiple first fuzzy subsets, the multiple first fuzzy subsets are weighted averaged to obtain a final integrated first fuzzy subset, which will eventually participate in the subsequent defuzzification. The higher the activation degree of the first fuzzy subset, the higher the corresponding influence on the final integrated first fuzzy subset.

[0101] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides an adaptive cruise control method. In the adaptive cruise control method, when the second fuzzy subset includes multiple, before step S02, the method further includes steps S0021 to S0022:

[0102] Step S0021: According to the membership value of the target fuzzy subset corresponding to the second fuzzy subset, the minimum membership value is determined as the activation degree of the second fuzzy subset.

[0103] In this embodiment, for each determined second fuzzy subset, there are two corresponding target fuzzy subsets, and the two target fuzzy subsets calculate their own membership values ​​when being determined as target fuzzy subsets. However, for each second fuzzy subset, the present application determines the smallest of the two membership values ​​corresponding to its two corresponding target fuzzy subsets as the activation degree corresponding to the second fuzzy subset.

[0104] Step S0022: According to the activation degree of each second fuzzy subset, weighted average is performed on all the second fuzzy subsets to obtain a final second fuzzy subset.

[0105] In this embodiment, based on the activation degrees of the determined multiple second fuzzy subsets, the multiple second fuzzy subsets are weighted averaged to obtain a final integrated second fuzzy subset, which will eventually participate in the subsequent defuzzification. The higher the activation degree of the second fuzzy subset, the higher the corresponding influence on the final integrated second fuzzy subset.

[0106] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides an adaptive cruise control method. In the adaptive cruise control method, step S1 may include: according to the driving state of the vehicle, calculating by a time headway determination algorithm to obtain the time headway value of the two vehicles, the time headway determination algorithm expression is: Among them, T thw represents the time interval between two vehicles, d represents the distance between two vehicles, and d c is the correction term, v is the speed of the vehicle; according to the driving state of the vehicle, the risk level determination algorithm is used to calculate and obtain the risk level of collision between the two vehicles. The risk level determination algorithm expression is: Among them, T ttc -1 Indicates the risk level, v rel represents the speed difference between the two vehicles, d represents the distance between the two vehicles, and v p Indicates the speed of the vehicle ahead.

[0107] In this embodiment, according to the vehicle driving state currently monitored, the time distance between two vehicles is calculated by the time distance determination algorithm, and the time distance between two vehicles is obtained. The expression of the time distance determination algorithm is: Among them, T thw represents the time interval between two vehicles, d represents the distance between two vehicles, and d c is the correction term, and v is the speed of the vehicle. At the same time, according to the vehicle driving status currently monitored, the risk level determination algorithm is used to calculate and obtain the risk level of collision between the two vehicles. The risk level determination algorithm expression is: Among them, T ttc -1 Indicates the risk level, v rel represents the speed difference between the two vehicles, d represents the distance between the two vehicles, and v p Indicates the speed of the vehicle ahead.

[0108] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides an adaptive cruise control method. In the adaptive cruise control method, before step S6, the method further includes step S061: determining the vehicle distance by integrating the spacing strategy, and the integration spacing strategy expression is: Among them, d exp Indicates the required workshop distance; d0 is the minimum safe distance between workshops; d c is the correction term; t h and c are fixed constant coefficients; v is the vehicle speed; v head is the speed of the pilot vehicle; v rel_head is the speed difference between the two vehicles.

[0109] In this embodiment, the adaptive cruise control of the CACC system also involves a vehicle spacing strategy, and the current vehicle spacing strategy mainly includes a fixed spacing (CTH Constant Time Headway) strategy and a variable spacing (VTH Variable Time Headway) strategy. The performance difference between the two is mainly reflected in convergence and adaptability. The CTH strategy means that its headway is fixed and unchanged, and its expected headway is only related to the vehicle speed, while the VTH strategy means that the headway changes with the changes in the surrounding environment, and its expected headway is not only related to the vehicle speed, but also to the acceleration and speed of the front vehicle. Although the VTH strategy has the advantages of being more flexible, safer, more comfortable and more efficient, the CTH strategy has higher stability and reliability than the VTH strategy, is closer to the driving habits of the driver, and has less communication requirements. The two have different advantages and disadvantages in different scenarios. In order to enable the vehicle to maintain the safety of following the vehicle and the comfort of speed changes under different working conditions, this application proposes a new fusion spacing strategy that comprehensively considers the fixed spacing (CTH Constant Time Headway) strategy and the variable spacing (VTH Variable Time Headway) strategy.

[0110] Specifically, the expression of the new fusion spacing strategy is: Among them, d exp Indicates the required workshop distance; d0 is the minimum safe distance between workshops; d c is the correction term; t h and c are fixed constant coefficients; v is the vehicle speed; v head is the speed of the pilot vehicle (i.e. the speed of the vehicle ahead that the self-vehicle is following); v rel_head is the speed difference between the two vehicles. Set d c The lower limit of is d0 to prevent the following distance from being too small, and the preferred value is 7m. The inter-vehicle distance is determined by the fusion distance strategy. The fusion distance strategy takes the overall movement trend of the vehicle and the team into consideration. While retaining the good convergence of the CTH strategy, the introduction of the VTH strategy can dynamically adjust the characteristics of the expected following distance, and according to the stability of the following vehicle, a p →0 when Δd→0 (a p is the acceleration of the front vehicle, Δd is the following vehicle distance error), and the stability of the fusion distance strategy is deduced. It can be determined that when the front vehicle fleet is driving stably, the following vehicle distance strategy has higher stability.

[0111] In the present application, when the method also includes step S061, step S6 includes: performing adaptive cruise control based on the first correction index weight, the second correction index weight and the required vehicle distance to be maintained / or performing adaptive cruise control based on the first correction index weight and the required vehicle distance to be maintained.

[0112] In this embodiment, in the case where the new fusion spacing strategy is introduced in the present application, the CACC system performs adaptive cruise control based on the first correction index weight, the second correction index weight and the determined vehicle distance to be maintained / or performs adaptive cruise control based on the first correction index weight and the determined vehicle distance to be maintained.

[0113] In this embodiment, the present application modifies the first indicator weight by the target value corresponding to the first correction variable. An optional implementation method for obtaining the first modified indicator weight is: by substituting the target value corresponding to the first correction variable into the expression Calculate and substitute the calculated value of w1 into the expression ω′1=a·w1 to calculate and obtain the corresponding first correction index weight. Among them, ω1 represents the first index weight, which is a known quantity in the CACC system; ω′1 represents the first correction index weight; C1 represents the target value corresponding to the first correction variable; a is a constant term. When the first index weight is the safety index weight, a preferably takes a value of 2. When the first index weight is the comfort index weight, a preferably takes a value of 0.6. The present application modifies the second index weight by the target value corresponding to the second correction variable. An optional implementation method for obtaining the second correction index weight is: by substituting the target value corresponding to the second correction variable into the expression Calculate and substitute the calculated value of w2 into the expression ω′2=b·w2 to calculate and obtain the corresponding second correction index weight. Wherein, in this calculation, ω2 represents the second index weight, which is a known quantity in the CACC system; ω′2 represents the second correction index weight; C2 represents the target value corresponding to the second correction variable; b is a constant term. When the second index weight is the safety index weight, b preferably takes a value of 2. When the second index weight is the comfort index weight, b preferably takes a value of 0.6. In this embodiment, through this implementation, the previously fixed index weight ω x =diag[ω safe ω vrel ω comf ], which will become the dynamically adjusted index weight ω based on the vehicle driving status x =diag[ω′ safe ω vrel ω′ comf ].

[0114] In this embodiment, based on general experience, the basic domain T of input and output is preferably limited. thw is [0,3]; T ttc -1 is [-0.3,0.3]; C safe and C comf is [-5,5].

[0115] In this embodiment, if Figure 3 and Figure 4 As shown, a specific implementation of an adaptive cruise control method provided by the present application can be to set a fuzzy controller on the input side of the upper controller of the CACC system, and the fuzzy controller is used to execute the steps of an adaptive cruise control method provided by the first aspect of the present application, specifically: based on the input (i.e., the time headway value T thw and risk level value T ttc -1 ), obtain the target fuzzy subsets corresponding to the two inputs respectively through fuzzification; then perform fuzzy reasoning based on the pre-established fuzzy rules to determine the first fuzzy subset corresponding to the first correction variable and / or the second fuzzy subset corresponding to the second correction variable, then defuzzify the first fuzzy subset and / or the second fuzzy subset to obtain the target value corresponding to the first correction variable and / or the target value corresponding to the second correction variable, and substitute the target value corresponding to the first correction variable into the expression Calculate and obtain the corresponding w1 value and / or substitute the target value corresponding to the second correction variable into the expression Calculate to get the corresponding w2 value; then input the obtained w1 value and / or the obtained w2 value to the upper-level controller, which calculates through the expression ω′1=a·w1 to get the corresponding first correction index weight and / or calculates through the expression ω′2=b·w2 to get the corresponding second correction index weight; finally, perform adaptive cruise control based on the obtained first correction index weight and / or second correction index weight.

[0116] Based on the same inventive concept, an embodiment of the present application provides an adaptive cruise control system, such as Figure 5 As shown, the system 500 includes:

[0117] The parameter determination module 501 is used to determine the time interval between two vehicles and the risk level of collision according to the driving status of the vehicles;

[0118] A target fuzzy subset determination module 502 is used to determine, according to the time headway value and the risk level value, a target fuzzy subset corresponding to the time headway value in the time headway fuzzy set and a target fuzzy subset corresponding to the risk level value in the risk level fuzzy set;

[0119] A first fuzzy subset determination module 503 is used to determine, according to a fuzzy rule, a first fuzzy subset corresponding to the target fuzzy subset in a first fuzzy set corresponding to a first correction variable, wherein the first correction variable is a correction variable corresponding to a first indicator weight, and the first indicator weight is a safety indicator weight or a comfort indicator weight;

[0120] A defuzzification processing module 504 is used to defuzzify the first fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the first correction variable;

[0121] A correction module 505, configured to correct the first indicator weight by using the target value corresponding to the first correction variable to obtain a first corrected indicator weight;

[0122] The cruise control module 506 is configured to perform adaptive cruise control based on the first correction index weight.

[0123] Optionally, the system 500 further includes:

[0124] A second fuzzy subset determination module is used to determine, according to a fuzzy rule, a second fuzzy subset corresponding to the target fuzzy subset in a second fuzzy set corresponding to a second correction variable, wherein the second correction variable is a correction variable corresponding to a second indicator weight, and the second indicator weight is an indicator weight different from the first indicator weight among a safety indicator weight and a comfort indicator weight;

[0125] a defuzzification processing module, configured to defuzzify the second fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the second correction variable;

[0126] A correction module, used for correcting the second indicator weight according to the target value corresponding to the second correction variable to obtain a second corrected indicator weight;

[0127] The first cruise control module is configured to perform adaptive cruise control based on the first correction index weight and the second correction index weight.

[0128] Optionally, the target fuzzy subset determination module 502 includes:

[0129] A first target fuzzy subset determination module is used to calculate the membership value under the time headway value according to the time headway value by using the membership function of each fuzzy subset in the time headway fuzzy set, and determine the fuzzy subset corresponding to the maximum membership value as the target fuzzy subset corresponding to the time headway value;

[0130] The second target fuzzy subset determination module is used to calculate the membership values ​​under the risk level value according to the risk level value through the membership functions of each fuzzy subset in the risk level fuzzy set, and determine the fuzzy subset corresponding to the maximum membership value as the target fuzzy subset corresponding to the risk level value.

[0131] Optionally, the first fuzzy subset determination module 503 is used to determine, according to fuzzy rules, a fuzzy subset in the first fuzzy set corresponding to the first correction variable that is simultaneously corresponding to the target fuzzy subset corresponding to the time interval between the workshops and the value and the target fuzzy subset corresponding to the risk level value, wherein the fuzzy rule defines the correspondence between the fuzzy subsets in the first fuzzy set and the fuzzy subsets in the time interval between the workshops and the value and the fuzzy subsets in the risk level fuzzy set; and to determine the fuzzy subset in the determined first fuzzy set that is simultaneously corresponding to the target fuzzy subset corresponding to the time interval between the workshops and the value and the target fuzzy subset corresponding to the risk level value as the first fuzzy subset corresponding to the first correction variable.

[0132] Optionally, a second fuzzy subset determination module is used to determine, according to fuzzy rules, a fuzzy subset in the second fuzzy set corresponding to the second correction variable that corresponds to both the target fuzzy subset corresponding to the time interval value and the target fuzzy subset corresponding to the risk level value, wherein the fuzzy rule defines the correspondence between the fuzzy subsets in the second fuzzy set and both the fuzzy subsets in the time interval fuzzy set and the fuzzy subsets in the risk level fuzzy set; and to determine the fuzzy subset in the determined second fuzzy set that corresponds to both the target fuzzy subset corresponding to the time interval value and the target fuzzy subset corresponding to the risk level value as the second fuzzy subset corresponding to the second correction variable.

[0133] Optionally, the system 500 further includes:

[0134] A first activation degree determining module, configured to determine, when the first fuzzy subset includes a plurality of members, a minimum membership value as the activation degree of the first fuzzy subset according to the membership value of the target fuzzy subset corresponding to the first fuzzy subset;

[0135] A first fuzzy subset determination module 503 is used to obtain a final first fuzzy subset by performing weighted averaging on all first fuzzy subsets according to the activation degree of each first fuzzy subset;

[0136] A second activation degree determination module is used to determine the minimum membership value as the activation degree of the second fuzzy subset according to the membership value of the target fuzzy subset corresponding to the second fuzzy subset when the second fuzzy subset includes multiple fuzzy subsets;

[0137] The second fuzzy subset determination module is used to perform weighted averaging on all the second fuzzy subsets according to the activation degree of each second fuzzy subset to obtain a final second fuzzy subset.

[0138] Optionally, the system 500 further includes:

[0139] The vehicle distance determination module is used to determine the vehicle distance by integrating the spacing strategy. The integration spacing strategy expression is: Among them, d exp Indicates the required workshop distance; d0 is the minimum safe distance between workshops; d c is the correction term; t h and c are fixed constant coefficients; v is the vehicle speed; v head is the speed of the pilot vehicle; v rel_head is the speed difference between the two vehicles;

[0140] The second cruise control module is configured to perform adaptive cruise control based on the first correction index weight, the second correction index weight, and the required vehicle-to-vehicle distance to be maintained.

[0141] Optionally, the parameter determination module 501 includes:

[0142] The vehicle headway value determination module is used to calculate the vehicle headway value between two vehicles through the vehicle headway determination algorithm according to the vehicle driving status. The vehicle headway determination algorithm expression is: Among them, T thw represents the time interval between two vehicles, d represents the distance between two vehicles, and d c is the correction term, v is the speed of the vehicle;

[0143] The risk level determination module is used to calculate the risk level of collision between two vehicles through a risk level determination algorithm according to the driving status of the vehicles. The risk level determination algorithm expression is: Among them, T ttc -1 Indicates the risk level, v rel represents the speed difference between the two vehicles, d represents the distance between the two vehicles, and v p Indicates the speed of the vehicle ahead.

[0144] Based on the same inventive concept, an embodiment of the present application provides a vehicle, which is provided with an adaptive cruise control system provided by the second aspect of the present application, and is used to execute the steps in an adaptive cruise control method provided by the first aspect of the present application.

[0145] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the system embodiment.

[0146] It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0147] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0148] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0149] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0153] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0154] The above is a detailed introduction to an adaptive cruise control method, system and vehicle provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for a person skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An adaptive cruise control method, characterized in that: The method comprises: According to the driving status of the vehicles, the time distance between the two vehicles and the risk level of collision are determined; According to the workshop time distance value and the risk level value, determine a target fuzzy subset corresponding to the workshop time distance value in a workshop time distance fuzzy set and a target fuzzy subset corresponding to the risk level value in a risk level fuzzy set; According to the fuzzy rule, determining a first fuzzy subset corresponding to the target fuzzy subset in a first fuzzy set corresponding to a first correction variable, wherein the first correction variable is a correction variable corresponding to a first indicator weight, and the first indicator weight is a safety indicator weight or a comfort indicator weight; Defuzzifying the first fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the first correction variable; Correcting the first indicator weight by using the target value corresponding to the first correction variable to obtain a first corrected indicator weight; Adaptive cruise control is performed based on the first correction index weight.

2. The adaptive cruise control method according to claim 1, characterized in that: Before performing adaptive cruise control based on the first correction index weight, the method further includes: According to the fuzzy rule, a second fuzzy subset corresponding to the target fuzzy subset in a second fuzzy set corresponding to a second correction variable is determined, wherein the second correction variable is a correction variable corresponding to a second index weight, and the second index weight is an index weight different from the first index weight among a safety index weight and a comfort index weight; Defuzzifying the second fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the second correction variable; The second indicator weight is corrected by the target value corresponding to the second correction variable to obtain a second corrected indicator weight; The performing adaptive cruise control based on the first correction index weight includes: performing adaptive cruise control based on the first correction index weight and the second correction index weight.

3. The adaptive cruise control method according to claim 2, characterized in that: According to the workshop time distance value and the risk level value, determining a target fuzzy subset corresponding to the workshop time distance value in the workshop time distance fuzzy set and a target fuzzy subset corresponding to the risk level value in the risk level fuzzy set, including: According to the time headway value, the membership value under the time headway value is calculated respectively by using the membership function of each fuzzy subset in the time headway fuzzy set, and the fuzzy subset corresponding to the maximum membership value is determined as the target fuzzy subset corresponding to the time headway value; According to the risk level value, the membership value under the risk level value is calculated respectively by the membership function of each fuzzy subset in the risk level fuzzy set, and the fuzzy subset corresponding to the maximum membership value is determined as the target fuzzy subset corresponding to the risk level value.

4. The adaptive cruise control method according to claim 1, characterized in that: Determining, according to the fuzzy rule, a first fuzzy subset corresponding to the target fuzzy subset in the first fuzzy set corresponding to the first correction variable, comprising: According to fuzzy rules, a fuzzy subset corresponding to the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value in the first fuzzy set corresponding to the first correction variable is determined, wherein the fuzzy rule defines a corresponding relationship between the fuzzy subsets in the first fuzzy set and the fuzzy subsets in the time headway fuzzy set and the fuzzy subsets in the risk level fuzzy set; The fuzzy subset corresponding to both the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value in the determined first fuzzy set is determined as the first fuzzy subset corresponding to the first correction variable.

5. The adaptive cruise control method according to claim 2, characterized in that: Determining, according to the fuzzy rule, a second fuzzy subset corresponding to the target fuzzy subset in the second fuzzy set corresponding to the second correction variable, comprising: Determine, according to a fuzzy rule, a fuzzy subset in the second fuzzy set corresponding to the second correction variable that corresponds to both the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value, wherein the fuzzy rule defines a corresponding relationship between the fuzzy subset in the second fuzzy set and both the fuzzy subset in the time headway fuzzy set and the fuzzy subset in the risk level fuzzy set; The fuzzy subset corresponding to both the target fuzzy subset corresponding to the time headway value and the target fuzzy subset corresponding to the risk level value in the determined second fuzzy set is determined as the second fuzzy subset corresponding to the second correction variable.

6. The adaptive cruise control method according to claim 3, characterized in that: In the case where the first fuzzy subset includes a plurality of fuzzy subsets, before defuzzifying the first fuzzy subset by a defuzzification algorithm to obtain a target value corresponding to the first correction variable, the method further includes: According to the membership value of the target fuzzy subset corresponding to the first fuzzy subset, the minimum membership value is determined as the activation degree of the first fuzzy subset; According to the activation degree of each first fuzzy subset, all the first fuzzy subsets are weighted averaged to obtain the final first fuzzy subset; In the case where the second fuzzy subset includes a plurality of sets, before defuzzifying the second fuzzy subset by a defuzzification algorithm to obtain a target value corresponding to the second correction variable, the method further includes: According to the membership value of the target fuzzy subset corresponding to the second fuzzy subset, the minimum membership value is determined as the activation degree of the second fuzzy subset; According to the activation degree of each second fuzzy subset, all the second fuzzy subsets are weighted averaged to obtain the final second fuzzy subset.

7. The adaptive cruise control method according to claim 1, characterized in that: Before performing adaptive cruise control based on the first correction index weight and the second correction index weight, the method further includes: The vehicle distance is determined by integrating the spacing strategy, and the integration spacing strategy expression is: Among them, d exp Indicates the required workshop distance; d0 is the minimum safe distance between workshops; d c is the correction term; t h and c are fixed constant coefficients; v is the vehicle speed; v head is the speed of the pilot vehicle; v rel_head is the speed difference between the two vehicles; The performing of adaptive cruise control based on the first correction index weight and the second correction index weight includes: performing adaptive cruise control based on the first correction index weight, the second correction index weight and the required vehicle-to-vehicle distance to be maintained.

8. The adaptive cruise control method according to claim 1, characterized in that: According to the driving status of the vehicles, the time distance between the two vehicles and the risk level of collision are determined, including: According to the vehicle driving status, the time distance between two vehicles is calculated by the time distance determination algorithm, and the time distance between two vehicles is obtained. The expression of the time distance determination algorithm is: Among them, T thw represents the time interval between two vehicles, d represents the distance between two vehicles, and d c is the correction term, v is the speed of the vehicle; According to the driving status of the vehicles, the risk level determination algorithm is used to calculate and obtain the risk level of collision between the two vehicles. The risk level determination algorithm expression is: Among them, T ttc -1 Indicates the risk level, v rel represents the speed difference between the two vehicles, d represents the distance between the two vehicles, and v p Indicates the speed of the vehicle ahead.

9. An adaptive cruise control system, characterized in that: The system comprises: A parameter determination module, used to determine the time interval between two vehicles and the risk level of collision according to the driving status of the vehicles; A target fuzzy subset determination module is used to determine, according to the workshop time distance value and the risk level value, a target fuzzy subset corresponding to the workshop time distance value in the workshop time distance fuzzy set and a target fuzzy subset corresponding to the risk level value in the risk level fuzzy set; A first fuzzy subset determination module is used to determine, according to a fuzzy rule, a first fuzzy subset corresponding to the target fuzzy subset in a first fuzzy set corresponding to a first correction variable, wherein the first correction variable is a correction variable corresponding to a first indicator weight, and the first indicator weight is a safety indicator weight or a comfort indicator weight; a defuzzification processing module, configured to defuzzify the first fuzzy subset by using a defuzzification algorithm to obtain a target value corresponding to the first correction variable; A correction module, used for correcting the first indicator weight according to the target value corresponding to the first correction variable to obtain a first corrected indicator weight; A cruise control module is configured to perform adaptive cruise control based on the first correction index weight.

10. A vehicle, characterized in that: The vehicle is provided with an adaptive cruise control system according to claim 9, which is used to execute the steps in an adaptive cruise control method according to any one of claims 1 to 8.

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