Method and system for estimating road adhesion coefficient of intelligent networked automobile based on V2V (Vehicle to Vehicle)

Abstract

The invention discloses a V2V-based intelligent networked automobile road adhesion coefficient estimation method and system, and relates to the technical field of intelligent networked automobiles. The method comprises the steps that physical parameters and motion state information of a front vehicle are received based on V2V, an event triggering communication mechanism is adopted in the sending process of the physical parameters and the motion state information of the front vehicle, and meanwhile data packet loss is considered; constructing a three-degree-of-freedom vehicle dynamics model of the front vehicle; and establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss. According to the method, the front vehicle is regarded as a sensor of the vehicle, the parameters and motion information of the front vehicle are obtained through vehicle-mounted wireless communication, the problems of data packet loss and limited communication bandwidth during communication are considered, the robustness of an estimator to model parameter perturbation is improved through a strong tracking algorithm, and the estimation accuracy is improved. And finally, establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss, and ensuring that the road adhesion coefficient is accurately obtained in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent connected vehicles, and specifically to a method and system for estimating the road surface adhesion coefficient of intelligent connected vehicles based on V2V. Background Art

[0002] The road surface adhesion coefficient is a key parameter directly determining the adhesion limit of vehicle tire forces. When a vehicle is driving on a road surface with a low adhesion coefficient (such as an ice and snow road surface), dangerous phenomena such as sideslip and fishtailing are more likely to occur. Through the powerful information acquisition ability of intelligent connected vehicles, road surface adhesion information can be obtained in a timely manner to take necessary measures to prevent the occurrence of dangerous working conditions. Therefore, the research on the estimation of the road surface adhesion coefficient is crucial for vehicle driving safety. However, the existing road surface adhesion coefficient estimation methods mainly use on-vehicle sensors of the vehicle itself to identify or estimate the adhesion coefficient of the currently passing road surface, and cannot obtain the adhesion coefficient of the road surface ahead that has not been passed, thus unable to ensure that the vehicle takes safety measures in advance. For this reason, the present invention proposes a method and system for estimating the road surface adhesion coefficient of intelligent connected vehicles based on V2V. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for estimating the road surface adhesion coefficient of intelligent connected vehicles based on V2V, which can use the vehicle ahead to obtain accurate road surface adhesion coefficients in advance.

[0004] According to the first aspect of the present invention, to achieve the above object, the present invention provides the following technical solution: A method for estimating the road surface adhesion coefficient of an intelligent connected vehicle based on V2V, comprising the following steps:

[0005] Receive the physical parameters and motion state information of the vehicle ahead based on V2V. During the transmission process of the physical parameters and motion state information of the vehicle ahead, an event-triggered communication mechanism is adopted, and data packet loss is considered at the same time;

[0006] Construct a non-linear three-degree-of-freedom vehicle dynamics model, a three-degree-of-freedom single-track model, and a brushed tire model of the vehicle ahead according to the physical parameters and motion state information of the vehicle ahead;

[0007] Based on the non-linear three-degree-of-freedom vehicle dynamics model, the three-degree-of-freedom single-track model, and the brushed tire model, construct an event-triggered strong tracking unscented Kalman filter considering data packet loss to form an improved estimator, and use the improved estimator to estimate the road surface adhesion coefficient.

[0008] Further, the physical parameters and motion state information of the vehicle ahead are collected through on-vehicle sensors of the vehicle ahead. The physical parameters and motion state information of the vehicle ahead specifically include the mass, moment of inertia, wheelbase, front wheel angle, rotational speed of each wheel, longitudinal acceleration, and lateral acceleration of the vehicle ahead. The longitudinal acceleration and lateral acceleration are defined as the measured value z.

[0009] Furthermore, based on the physical parameters and motion state information of the preceding vehicle received by V2V, an event-triggered communication mechanism is adopted during the transmission of the physical parameters and motion state information of the preceding vehicle, and data packet loss is also considered, as follows:

[0010] (31) Use η k to describe whether the preceding vehicle sends data:

[0011]

[0012] In the formula, ε represents the event-triggering threshold; represents the measurement value of the preceding vehicle's sensor received by the host vehicle at time k; z k represents the latest sensor measurement value of the preceding vehicle at time k; when η k = 1, the preceding vehicle will send the current latest sensor measurement information to the host vehicle through V2V, and the information received by the host vehicle is Conversely, the host vehicle believes that

[0013] (32) Use the random variable θ k to model data packet loss. The measurement noise v k relative to λ k The probability density function is defined as:

[0014]

[0015] In the formula, σ→∞; I is the identity matrix; R k is the covariance matrix of the measurement noise; when θ k = 1, the sensor information is not lost, and the host vehicle receives the measurement information sent by the preceding vehicle; conversely, data packet loss occurs, activating the event-triggered communication mechanism. At this time, the host vehicle cannot receive the latest measurement information and will use the measurement information of the previous moment for estimation.

[0016] Furthermore, a non-linear three-degree-of-freedom vehicle dynamics model, a three-degree-of-freedom single-track model, and a brushed tire model of the preceding vehicle are constructed, as follows:

[0017] (41) The non-linear three-degree-of-freedom vehicle dynamics model of the preceding vehicle is:

[0018]

[0019] In the formula, i = f, r, respectively representing the front wheel and the rear wheel, and j = l, r, respectively representing the left wheel and the right wheel; F xij represents the longitudinal force, and F yij represents the lateral force; a x , a y represent the longitudinal acceleration and the lateral acceleration; γ represents the yaw rate; l a , lb Denote the distances from the centroid to the front axle and from the centroid to the rear axle; l f , l r Denote the front track width and the rear track width respectively; m, I z Denote the mass of the leading vehicle and the moment of inertia about the z-axis; δ f Is the front wheel steering angle

[0020] (42) Under the assumption of a linear tire model, a three-degree-of-freedom single-track vehicle dynamics model can be obtained:

[0021]

[0022] In the formula, C f , C r Are the cornering stiffnesses of the front axle and the rear axle respectively; v x Denotes the longitudinal vehicle speed of the leading vehicle; β is the centroid side slip angle of the leading vehicle;

[0023] (43) Establish a brushed tire model, the formula is as follows:

[0024]

[0025] In the formula, C x , C y Are the longitudinal and lateral stiffnesses of the tire respectively; β, κ ij , α ij Are the centroid side slip angle, slip ratio and tire cornering angle of the leading vehicle respectively; ω ij Is the rotational speed of the wheel; v x Is the longitudinal speed of the vehicle; F zij Is the vertical load of the tire, j = l, r; h, L, R are the height of the centroid, wheelbase and radius of the wheel respectively.

[0026] Furthermore, construct an event-triggered strong tracking unscented Kalman filter considering data packet loss to form an improved estimator, specifically as follows:

[0027] (51) Let the state equation and measurement equation of the discrete linear system be:

[0028]

[0029] In the formula, k represents the k-th moment, k + 1 represents the (k + 1)-th moment, f() and h() are the state equation and measurement equation containing noise respectively; x k Is the state variable, u k Is the control input, z k Is the observed variable; w k And v k Are the process noise and measurement noise respectively, and the two are independent zero-mean Gaussian noise sequences;

[0030] (52) The covariance matrix is:

[0031]

[0032] Where Q k and R k are the covariance matrix of the process noise and the covariance matrix of the measurement noise respectively, E() is to calculate the mean value, and the superscript T represents the transpose of the matrix

[0033] Let H k be the Jacobian matrix of h(·) in the measurement equation:

[0034]

[0035] Then the event-triggered strong tracking unscented Kalman filter considering packet loss is as follows:

[0036] (52.1) Initialization: Initialize the estimated state and the estimated error covariance matrix Initialize the mean weight and the covariance weight as:

[0037]

[0038] Where n is the dimension of the estimated state; λ = α 2 (n + l) - n is the scaling factor; α, is the scaling factor, satisfying 0 < α < 1, l is the second-order scaling factor, with a value of l = 3 - n;

[0039] (52.2) Time update:

[0040] Select 2n + 1 Sigma points:

[0041]

[0042] Transfer the Sigma point set:

[0043]

[0044] Calculate the prior state estimate and the prior error covariance matrix P k+1|k :

[0045]

[0046] (52.3) Measurement update:

[0047] Update the Sigma points according to the prior state estimate value

[0048]

[0049] Transfer Sigma points using the measurement equation:

[0050]

[0051] where, is the measured value represented by sigma points

[0052] Calculate the estimated measured value and the covariance matrix P z,k+1 :

[0053]

[0054] Calculate the cross-correlation matrix P xz,k+1 :

[0055]

[0056] Execute the strong tracking algorithm with the fading factor Φ k Update the predicted error covariance matrix P k+1|k :

[0057]

[0058] where, The calculation method is as follows:

[0059]

[0060] where Γ τ is a constant set according to the decay rate of the τ-th state of the system, ρ is the forgetting factor, and 0 < ρ ≤ 1; is the error between the actual measured value and the estimated measured value;

[0061] (53) Calculate the Kalman filter gain K k+1 and the correction gain L k+1 :

[0062]

[0063] Update the estimated state and the error covariance matrix:

[0064]

[0065] where, a1 and a2 are positive coefficients to be determined respectively.

[0066] Furthermore, use the improved estimator to estimate the road surface adhesion coefficient, specifically as follows:

[0067] The improved estimator consists of two event-triggered strong tracking unscented Kalman filters considering packet loss in series. The first estimator is used to estimate the motion state of the vehicle, denoted as the motion state estimator; the second estimator estimates the road adhesion coefficient by using the estimation result of the motion state estimator and the current measurement value, denoted as the adhesion coefficient estimator.

[0068] (61) For the motion state estimator, according to the three-degree-of-freedom single-track vehicle dynamics model and using the zero-order hold method for discretization with a discrete control period of ΔT, the estimation model is obtained:

[0069]

[0070] In the formula, the state vector is x 1,k =[β,γ,v x ,a x T ; the control input is u 1,k =[δ f ; the measurement signal is z 1,k =[a x ,a y , a x and a y follow the event-triggered communication mechanism and the data packet loss model; f1 is determined by the three-degree-of-freedom single-track vehicle dynamics model and f1(a x,k )=a x,k-1 , and h1 is determined by the three-degree-of-freedom single-track vehicle dynamics model and h1(a x,k )=a x,k ; w k and v k are the process noise and the measurement noise respectively;

[0071] (62) For the adhesion coefficient estimator, according to the nonlinear three-degree-of-freedom vehicle dynamics model and the brushed tire model and using the zero-order hold method for discretization with a discrete control period of ΔT, the estimation model is obtained:

[0072]

[0073] In the formula, the state vector is x 2,k =[μ fl ,μ fr ,μ rl ,μ rr T ; the control input is u 2,k =[β,γ,v x ,δ f ,ω ij ,F zij , which is used to calculate the longitudinal / lateral force according to the brushed tire model; the measurement output is​​ Only a x and a y follow the event-triggered communication mechanism and the data packet loss model, and are calculated based on the yaw rate obtained by the motion state estimator; f2 = I·x 2,k-1 , where h2 is determined by the non-linear three-degree-of-freedom vehicle dynamics model and the brushed tire model;

[0074] Based on the two discrete-time state space models described by Formula (34) and Formula (35), an improved estimator is used to estimate the road adhesion coefficient ahead.

[0075] According to the second aspect of the present invention, the present invention provides a road adhesion coefficient estimation system for intelligent connected vehicles based on V2V, which is used to implement the above-mentioned road adhesion coefficient estimation method for intelligent connected vehicles based on V2V, including:

[0076] A data receiving module, which is used to receive the physical parameters and motion state information of the vehicle ahead based on V2V, adopts the event-triggered communication mechanism during the transmission of the physical parameters and motion state information of the vehicle ahead, and simultaneously considers data packet loss;

[0077] A construction module, which is used to construct a non-linear three-degree-of-freedom vehicle dynamics model, a three-degree-of-freedom single-track model and a brushed tire model of the vehicle ahead;

[0078] An improved estimator construction module, which is used to construct an event-triggered strong tracking unscented Kalman filter considering data packet loss to form an improved estimator;

[0079] A road adhesion coefficient estimation module, which is used to estimate the road adhesion coefficient by using the improved estimator.

[0080] According to the third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the computer program stored in the memory is loaded and executed by the processor, the above-mentioned road adhesion coefficient estimation method for intelligent connected vehicles based on V2V is adopted.

[0081] According to the fourth aspect of the present invention, the present invention provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the above-mentioned road adhesion coefficient estimation method for intelligent connected vehicles based on V2V when executed by a computer processor.

[0082] According to the fifth aspect of the present invention, there is provided a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to load and execute the above-mentioned method for estimating the road adhesion coefficient of an intelligent connected vehicle based on V2V.

[0083] The present invention has at least the following beneficial effects:

[0084] 1. The method for estimating the road adhesion coefficient of an intelligent connected vehicle based on V2V provided by the present invention uses the vehicle in front as a sensor for the host vehicle, and realizes the early acquisition of the road adhesion coefficient through a dynamic method, providing a basis for vehicle safety decision-making;

[0085] 2. The method for estimating the road adhesion coefficient of an intelligent connected vehicle based on V2V provided by the present invention saves channel resources through an event-triggered mechanism during the communication with the vehicle in front, and considers the influence of data packet loss and parameter perturbation in the estimator design, improving the robustness of the road adhesion coefficient estimator.

[0086] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0087] Figure 1 is a schematic flowchart of the estimation method of the present invention

[0088] Figure 2 is a schematic framework principle diagram of the estimation method of the present invention;

[0089] Figure 3 is a schematic diagram of the V2V communication principle including an event-triggered communication mechanism and packet loss in the present invention;

[0090] Figure 4 is a schematic diagram of a nonlinear three-degree-of-freedom vehicle dynamics model in the present invention;

[0091] Figure 5 is a schematic diagram of a three-degree-of-freedom single-track vehicle dynamics model in the present invention. Detailed Embodiments

[0092] Next, the technical solutions in the embodiments of the present disclosure will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

[0093] Host vehicle: refers to the vehicle driven by oneself in the present invention;

[0094] Leading vehicle: refers to a vehicle that is traveling in front of the host vehicle along the driving direction.

[0095] Embodiment 1:

[0096] Please refer to Figure 1 and Figure 2 , the present invention provides a technical solution: a method for estimating the road adhesion coefficient of an intelligent connected vehicle based on V2V, including the following steps:

[0097] S1. Based on V2V, receive the physical parameters and motion state information of the leading vehicle. During the transmission of the physical parameters and motion state information of the leading vehicle, an event-triggered communication mechanism is adopted, and data packet loss is considered at the same time;

[0098] Obtain the physical parameters and motion state information (leading vehicle information) of the leading vehicle through V2V, including the mass, moment of inertia, wheelbase, front wheel angle, rotational speed of each wheel, longitudinal acceleration, and lateral acceleration of the leading vehicle, etc. The physical parameters (vehicle parameters) and motion state information (vehicle state) of the leading vehicle are collected through the in-vehicle sensors of the leading vehicle; define the longitudinal acceleration and lateral acceleration as the measured value z. When obtaining z through V2V communication, an event-triggered communication mechanism is adopted. The core of this mechanism is that the leading vehicle decides whether to send sensor data to the host vehicle according to the degree of change in the current sensor sampling value and the sampling value sent at the previous moment, thereby saving communication channel resources. If the sampling value changes greatly between the previous and current moments, the sampling value is sent through V2V, and the host vehicle uses the latest sampled data for estimation. Otherwise, it does not send, and the host vehicle uses the data at the previous moment and estimates according to a specific correction method, as follows: Figure 3 As shown, the core is that the leading vehicle decides whether to send sensor data to the host vehicle according to the degree of change in the current sensor sampling value and the sampling value sent at the previous moment, thereby saving communication channel resources. If the sampling value changes greatly between the previous and current moments, the sampling value is sent through V2V, and the host vehicle uses the latest sampled data for estimation. Otherwise, it does not send, and the host vehicle uses the data at the previous moment and estimates according to a specific correction method, as follows:

[0099] (S11) Use η k to describe whether the leading vehicle sends data:

[0100]

[0101] In the formula, ε represents the event trigger threshold; represents the sensor measurement value of the leading vehicle received by the host vehicle at time k; z k represents the latest sensor measurement value of the leading vehicle at time k; when η k = 1, the leading vehicle will send the current latest sensor measurement information to the host vehicle through V2V, and the information received by the host vehicle is Otherwise, the host vehicle believes that

[0102] (S12) When the leading vehicle uses V2V to send information, data may be lost, which is called packet loss. Therefore, a random variable θ k is used to model the data packet loss, and the measurement noise v k relative to λ k The probability density function of is defined as:

[0103]

[0104] where σ → ∞; I is the identity matrix; R k is the covariance matrix of the measurement noise; when θ k = 1, there is no loss of sensor information, and the host vehicle receives the measurement information sent by the leading vehicle; otherwise, a data packet loss occurs, activating the event-triggered communication mechanism. At this time, the host vehicle cannot receive the latest measurement information and will use the measurement information of the previous moment for estimation;

[0105] S2. Construct a non-linear three-degree-of-freedom vehicle dynamics model, a three-degree-of-freedom single-track model, and a brushed tire model for the leading vehicle, as follows:

[0106] (S21) As Figure 4 shown, the non-linear three-degree-of-freedom vehicle dynamics model of the leading vehicle is:

[0107]

[0108] where i = f, r, representing the front and rear wheels respectively, and j = l, r, representing the left and right wheels respectively; F xij represents the longitudinal force, and F yij represents the lateral force; a x , a y represent the longitudinal acceleration and lateral acceleration; γ represents the yaw angular velocity; l a , l b represent the distance from the center of mass to the front axle and the distance from the center of mass to the rear axle; l f , l r represent the front track width and rear track width respectively; m, I z represent the mass of the leading vehicle and the moment of inertia about the z-axis; δ f is the front wheel steering angle;

[0109] (S22) As Figure 5 shown, under the assumption of a linear tire model, the three-degree-of-freedom single-track vehicle dynamics model can be obtained:

[0110]

[0111] where C f , C r are the cornering stiffnesses of the front and rear axles respectively; v x represents the longitudinal vehicle speed of the leading vehicle; β is the sideslip angle of the center of mass of the leading vehicle;

[0112] (S23) Establish a brushed tire model, and the formula is as follows:

[0113]

[0114] wherein, C x , C y are respectively the longitudinal and lateral stiffnesses of the tire; β, κ ij , α ij are respectively the sideslip angle of the center of mass of the vehicle in front, the slip ratio, and the sideslip angle of the tire; ω ij is the rotational speed of the wheel; v x is the longitudinal speed of the vehicle; F zij is the vertical load of the tire, j = l, r; h, L, R are respectively the height of the center of mass, the wheelbase, and the radius of the wheel;

[0115] S3. Construct an event-triggered strong tracking unscented Kalman filter considering data packet loss to form an improved estimator, specifically as follows:

[0116] Construct an event-triggered strong tracking unscented Kalman filter considering data packet loss to form an improved estimator, specifically as follows:

[0117] (S31) Assume that the state equation and measurement equation of the discrete linear system are:

[0118]

[0119] wherein, k represents the k-th moment, k + 1 represents the (k + 1)-th moment, f() and h() are respectively the state equation and measurement equation containing noise; x k is the state variable, u k is the control input, z k is the observed variable; w k and v k are respectively the process noise and measurement noise, and the two are independent zero-mean Gaussian noise sequences;

[0120] (S32) The covariance matrix is:

[0121]

[0122] wherein, Q k and R k are respectively the covariance matrix of the process noise and the covariance matrix of the measurement noise, E() is to find the mean, and the superscript T represents the transpose of the matrix;

[0123] Let H k be the Jacobian matrix of h(·) in the measurement equation:

[0124]

[0125] Then the event-triggered strong tracking unscented Kalman filter considering packet loss is as follows:

[0126] (S32.1) Initialization: Initialize the estimated state and the estimated error covariance matrix Initialize the mean weight and the covariance weight as:

[0127]

[0128] where n is the dimension of the estimated state; λ = α 2 (n + l) - n is the scaling factor; α, is the scaling factor, satisfying 0 < α < 1, l is the second - order scaling factor, with a value of l = 3 - n;

[0129] (S32.2) Time update:

[0130] Select 2n + 1 Sigma points:

[0131]

[0132] Propagate the Sigma point set:

[0133]

[0134] Calculate the prior state estimate and the prior error covariance matrix P k+1|k :

[0135]

[0136] (S32.3) Measurement update:

[0137] Update the Sigma points according to the prior state estimate value

[0138]

[0139] Propagate the Sigma points using the measurement equation:

[0140]

[0141] where, is the measurement value represented by the sigma point;

[0142] Calculate the estimated measurement value and the covariance matrix P z,k+1 :

[0143]

[0144] Calculate the cross - correlation matrix P xz,k+1 :

[0145]

[0146] Execute the strong tracking algorithm and adopt the fading factor Φ k Update the predicted error covariance matrix P k+1|k :

[0147]

[0148] In the formula, The calculation method is as follows:

[0149]

[0150] In the formula, Γ τ is a constant set according to the decay rate of the τ-th state of the system, ρ is the forgetting factor, and 0 < ρ ≤ 1; is the error between the actual measurement value and the estimated measurement value;

[0151] (S33) Calculate the Kalman filter gain K k+1 and the correction gain L k+1 :

[0152]

[0153] Update the estimated state and the error covariance matrix:

[0154]

[0155] In the formula, a1 and a2 are respectively positive coefficients to be determined; I is the identity matrix;

[0156] S4. Use the improved estimator to estimate the road adhesion coefficient, specifically as follows:

[0157] The improved estimator includes two cascaded event-triggered strong tracking unscented Kalman filters considering packet loss. The first estimator is used to estimate the motion state of the vehicle, denoted as the motion state estimator; the second estimator uses the estimation result of the motion state estimator and the current measurement value to estimate the road adhesion coefficient, denoted as the adhesion coefficient estimator;

[0158] (S41) For the motion state estimator, according to the three-degree-of-freedom single-track vehicle dynamics model and using the zero-order hold method for discretization, with the discrete control period of ΔT, the estimation model is obtained:

[0159]

[0160] In the formula, the state vector is x 1,k = [β, γ, v x , a x T ; The control input is u 1,k ​= [δ f ; The measurement signal is z 1,k = [a x , a y , a x and a y follow the event-triggered communication mechanism and data packet loss model; f1 is determined by the three-degree-of-freedom single-rail vehicle dynamics model and f1(a x,k ) = a x,k-1 ; h1 is determined by the three-degree-of-freedom single-rail vehicle dynamics model and h1(a x,k ) = a x,k ; w k and v k are the process noise and measurement noise respectively;

[0161] (S42) For the adhesion coefficient estimator, according to the non-linear three-degree-of-freedom vehicle dynamics model and the brushed tire model, and using the zero-order hold method for discretization, the discrete control period is ΔT, and the estimation model is obtained:

[0162]

[0163] Where the state vector is x 2,k = [μ fl , μ fr , μ rl , μ rr T ; The control input is u 2,k = [β, γ, v x , δ f , ω ij , F zij , which is used to calculate the longitudinal / lateral force according to the brushed tire model; The measurement output is Where only a x and a y follow the event-triggered communication mechanism and data packet loss model, and are calculated based on the yaw rate obtained from the motion state estimator; f2 = I·x 2,k-1 , and h2 is determined by the non-linear three-degree-of-freedom vehicle dynamics model and the brushed tire model;

[0164] Based on the two discrete-time state space models described by formula (34) and formula (35), an improved estimator is used to estimate the adhesion coefficient of the road ahead.

[0165] ​In summary, the present invention regards the vehicle ahead as a sensor of the host vehicle, uses vehicle-mounted wireless communication to obtain the parameters and motion information of the vehicle ahead, considers the problems of data packet loss and limited communication bandwidth existing in the communication process, and improves the robustness of the estimator to model parameter perturbations through a strong tracking algorithm. Finally, a road surface adhesion coefficient estimator is established based on an event-triggered strong tracking unscented Kalman filter considering packet loss, ensuring the accurate acquisition of the road surface adhesion coefficient in advance.

[0166] Embodiment 2:

[0167] This embodiment provides a road surface adhesion coefficient estimation system for intelligent connected vehicles based on V2V, which is used to implement the above-mentioned road surface adhesion coefficient estimation method for intelligent connected vehicles based on V2V, and includes:

[0168] A data receiving module, which is used to receive the physical parameters and motion state information of the vehicle ahead based on V2V, adopts an event-triggered communication mechanism during the sending process of the physical parameters and motion state information of the vehicle ahead, and at the same time considers data packet loss;

[0169] A construction module, which is used to construct a non-linear three-degree-of-freedom vehicle dynamics model, a three-degree-of-freedom single-track model and a brushed tire model of the vehicle ahead;

[0170] An improved estimator construction module, which is used to construct an event-triggered strong tracking unscented Kalman filter considering data packet loss to form an improved estimator;

[0171] A road surface adhesion coefficient estimation module, which is used to estimate the road surface adhesion coefficient by using the improved estimator.

[0172] Specifically, the above-mentioned data receiving module, construction module, improved estimator construction module and road surface adhesion coefficient estimation module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of accurately obtaining the road surface adhesion coefficient in advance by using the vehicle ahead according to the above-provided road surface adhesion coefficient estimation method for intelligent connected vehicles based on V2V; the above-mentioned data receiving module, construction module, improved estimator construction module and road surface adhesion coefficient estimation module can perform operations according to the specific steps given by the above-mentioned road surface adhesion coefficient estimation method for intelligent connected vehicles based on V2V.

[0173] It should be noted that the division of each module of the above system is only a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or physically separated. Moreover, these modules can all be implemented in the form of software called by processing elements; they can also all be implemented in the form of hardware; or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the data receiving module can be a separately established processing element, or can be integrated in a certain chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and the function of the above signal processing module can be called and executed by a certain processing element of the above device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together or can be independently implemented. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuit of the hardware in the processor element or the instructions in the form of software.

[0174] For example, the above modules can be one or more integrated circuits configured to implement the above method, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), etc. Again, when a certain module above is implemented in the form of a processing element scheduling program code, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program code. Again, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0175] Embodiment 3:

[0176] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor. When the processor loads and executes the computer program, the above method for estimating the road surface adhesion coefficient of an intelligent connected vehicle based on V2V is adopted.

[0177] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may further include input / output devices, network access devices, and a bus, etc.

[0178] Furthermore, the processor can be a central processing unit (CPU). Of course, according to the actual usage situation, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc. This application does not make any restrictions in this regard.

[0179] Embodiment 4:

[0180] The present invention provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the above-mentioned method for estimating the road surface adhesion coefficient of an intelligent connected vehicle based on V2V when executed by a computer processor.

[0181] Among them, the computer program can be stored in a computer-readable medium. The computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some middleware form, etc. The computer-readable medium includes any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0182] Embodiment 5:

[0183] The present invention provides a computer program product, and the computer program product includes a computer program. When the computer program is executed by a processor, it is used to load and execute the above-mentioned method for estimating the road surface adhesion coefficient of an intelligent connected vehicle based on V2V.

[0184] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0185] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed to" or "disposed on" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation.

[0186] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0187] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

Claims

1. A method for estimating road adhesion coefficient of intelligent connected vehicles based on V2V, characterized in that: The following steps are involved: Based on V2V receiving the physical parameters and motion status information of the preceding vehicle, an event-triggered communication mechanism is adopted in the process of sending the physical parameters and motion status information of the preceding vehicle, while taking data packet loss into consideration; According to the physical parameters and motion state information of the front vehicle, a nonlinear three-degree-of-freedom vehicle dynamics model, a three-degree-of-freedom single track model and a brushed tire model of the front vehicle are constructed; Based on the nonlinear three-degree-of-freedom vehicle dynamics model, the three-degree-of-freedom single-track model and the brushed tire model, an event-triggered strong tracking unscented Kalman filter considering data packet loss is constructed to form an improved estimator, and the road adhesion coefficient is estimated using the improved estimator.

2. The method for estimating road adhesion coefficient of a V2V-based intelligent connected vehicle according to claim 1, characterized in that: The physical parameters and motion state information of the leading vehicle are collected and acquired through the on-board sensors of the leading vehicle. The physical parameters and motion state information of the leading vehicle specifically include the mass, moment of inertia, wheelbase, front wheel angle, rotation speed of each wheel, longitudinal acceleration and lateral acceleration of the leading vehicle. The longitudinal acceleration and lateral acceleration are defined as the measurement value z.

3. The method for estimating road adhesion coefficient of V2V-based intelligent connected vehicles according to claim 2, characterized in that: Based on V2V receiving the physical parameters and motion status information of the preceding vehicle, an event-triggered communication mechanism is adopted in the process of sending the physical parameters and motion status information of the preceding vehicle, while considering data packet loss, as follows: (31) Using η k Describes whether the preceding vehicle sends data: In the formula, ε represents the event triggering threshold; represents the sensor measurement value of the preceding vehicle received by the vehicle at time k; z k represents the latest sensor measurement value of the preceding vehicle at time k; when η k = 1, the preceding vehicle sends the latest sensor measurement information to the ego vehicle via V2V, and the ego vehicle receives the following information: On the contrary, the car thinks (32) Using random variable θ k Model the data packet loss and measure the noise v k Relative to λ k The probability density function of is defined as: Where, σ→∞; I is the unit matrix; R k is the covariance matrix of the measurement noise; when θ k =1, the sensor information is not lost, and the ego vehicle receives the measurement information sent by the preceding vehicle; On the contrary, if data packet loss occurs and the event triggers the communication mechanism, the vehicle cannot receive the latest measurement information at this time and will use the measurement information of the previous moment for estimation.

4. The method for estimating road adhesion coefficient of V2V-based intelligent connected vehicles according to claim 3, characterized in that: Construct the nonlinear three-degree-of-freedom vehicle dynamics model, three-degree-of-freedom single-track model and brushed tire model of the front vehicle as follows: (41) The nonlinear three-degree-of-freedom vehicle dynamics model of the front vehicle is: In the formula, i=f,r, respectively represent the front wheel and rear wheel, j=l,r, respectively represent the left wheel and right wheel; F xij Denotes the longitudinal force, F yij Indicates lateral force; a x ,a y represents the longitudinal acceleration and lateral acceleration; γ represents the yaw angular velocity; l a ,l b Indicates the distance from the center of mass to the front axle and the center of mass to the rear axle; l f ,l r Respectively represent the front and rear track; m, I z represents the mass of the front vehicle and its moment of inertia around the z-axis; δ f is the front wheel turning angle; (42) Under the assumption of a linear tire model, the three-degree-of-freedom single-track vehicle dynamics model is obtained: In the formula, C f ,C r are the cornering stiffness of the front and rear axles respectively; v x represents the longitudinal speed of the front vehicle; β is the sideslip angle of the center of mass of the front vehicle; (43) Establish a brushed tire model, the formula is as follows: In the formula, C x ,C y are the longitudinal and lateral stiffness of the tire respectively; β,κ ij ,α ij are the center of mass slip angle, slip rate and tire slip angle of the front vehicle respectively; ω ij is the rotation speed of the wheel; F zij is the vertical load of the tire, j=l,r; h, L, R are the height of the center of mass, wheelbase and radius of the wheel respectively.

5. The method for estimating road adhesion coefficient of V2V-based intelligent connected vehicle according to claim 4, characterized in that: Construct an event-triggered strong tracking unscented Kalman filter that takes into account data packet loss to form an improved estimator, as follows: (51) Assume that the state equation and measurement equation of the discrete linear system are: Where k represents time k, k+1 represents time k+1, f() and h() are the state equation and measurement equation containing noise respectively; x k is the state variable, u k is the control input, z k is the observed variable; w k and v k are process noise and measurement noise respectively, and both are independent zero-mean Gaussian noise sequences; (52)The covariance matrix is: In the formula, Q k and R k are the covariance matrices of process noise and measurement noise, respectively. E() is the mean, and the superscript T indicates the transpose of the matrix. Let H k is the Jacobian matrix of h(·) in the measurement equation: Then consider the packet loss event to trigger the strong tracking unscented Kalman filter as follows: (52.1) Initialization: Initialize the estimated state and the estimated error covariance matrix Initialize mean weights With covariance weights for: Where n is the dimension of the estimated state; λ = α 2 (n+l)-n is the scale factor; α, is the scaling factor, satisfying 0<α<1, l is the second-order proportional factor, and its value is l=3-n; (52.2) Time update: Selected 2n+1 Sigma points: Pass the Sigma point set: Compute a priori state estimates and the prior error covariance matrix P k+1|k : (52.3) Measurement update: Update the Sigma point based on the prior state estimate Transfer Sigma points using the measurement equation: In the formula, is the measured value represented by the sigma point; Calculate estimated measurements and the covariance matrix P z,k+1 : Calculate the cross-correlation matrix P xz,k+1 : Perform a strong tracking algorithm with a fading factor Φ k Update the prediction error covariance matrix P k+1|k : In the formula, The calculation is as follows: Where Γ τ is a constant set according to the decay rate of the τth state of the system, ρ is the forgetting factor, which is 0<ρ≤1; is the error between the actual measured value and the estimated measured value; (53) Calculate the Kalman filter gain K k+1 And the correction gain L k+1 : Update the estimated state and error covariance matrices: Where a1 and a2 are the positive coefficients to be determined; I is the unit matrix.

6. The method for estimating road adhesion coefficient of V2V-based intelligent connected vehicles according to claim 5, characterized in that: The improved estimator is used to estimate the road adhesion coefficient as follows: The improved estimator includes two event-triggered strong tracking unscented Kalman filters in series considering packet loss, wherein the first estimator is used to estimate the motion state of the vehicle, denoted as the motion state estimator; the second estimator uses the estimation result of the motion state estimator and the current measurement value to estimate the road adhesion coefficient, denoted as the adhesion coefficient estimator; (61) For the motion state estimator, based on the three-degree-of-freedom single-track vehicle dynamics model, the zero-order hold method is used for discretization, and the discrete control period is ΔT, and the estimation model is obtained: In the formula, the state vector is x 1,k =[β,γ,v x ,a x ] T ; The control input is u 1,k =[δ f ]; the measured signal is z 1,k =[a x ,a y ], a x and a y Following the event-triggered communication mechanism and data packet loss model; f1 is composed of a three-degree-of-freedom single-track vehicle dynamics model and f1(a x,k )=a x,k-1 Determine, h1 is determined by the three-degree-of-freedom single-track vehicle dynamics model and h1(a x,k )=a x,k OK; w k and v k are process noise and measurement noise respectively; (62) For the adhesion coefficient estimator, based on the nonlinear three-degree-of-freedom vehicle dynamics model and the brushed tire model, the zero-order hold method is used for discretization, and the discrete control period is ΔT, and the estimation model is obtained: Where the state vector is x 2,k =[μ fl ,μ fr ,μ rl ,μ rr ] T ; The control input is u 2,k =[β,γ,v x ,δ f ,ω ij ,F zij ], used to calculate the longitudinal / lateral forces based on the brushed tire model; the measured output is , where only a x and a y Following the event-triggered communication mechanism and data packet loss model, Calculated based on the yaw rate obtained by the motion state estimator; f2 = I·x 2,k-1 , h2 is determined by the nonlinear three-degree-of-freedom vehicle dynamics model and the brushed tire model; Based on the two discrete time state space models described by formula (34) and formula (35), the estimation of the front road adhesion coefficient is realized using an improved estimator.

7. A V2V-based intelligent connected vehicle road adhesion coefficient estimation system, used to implement the V2V-based intelligent connected vehicle road adhesion coefficient estimation method according to any one of claims 1 to 6, characterized in that: include: A data receiving module is used to receive the physical parameters and motion status information of the preceding vehicle based on V2V. An event-triggered communication mechanism is used in the process of sending the physical parameters and motion status information of the preceding vehicle, while taking data packet loss into consideration; A construction module for constructing a nonlinear three-degree-of-freedom vehicle dynamics model, a three-degree-of-freedom single-track model, and a brushed tire model of the front vehicle; Improved estimator building module, used to build an event-triggered strong tracking unscented Kalman filter that takes data packet loss into account, forming an improved estimator; The road adhesion coefficient estimation module is used to estimate the road adhesion coefficient using an improved estimator.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the method for estimating the road adhesion coefficient of a V2V-based intelligent connected vehicle according to any one of claims 1 to 6 is adopted.

9. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the road adhesion coefficient estimation method for a V2V-based intelligent connected vehicle as described in any one of claims 1 to 6 when executed by a computer processor.

10. A computer program product, characterized in that The computer program product includes a computer program, which, when executed by a processor, is used to load and execute the method for estimating the road adhesion coefficient of a V2V-based intelligent connected vehicle according to any one of claims 1 to 6.

Images