Vehicle platoon road friction estimation method and device combined with extended kalman filter

Abstract

The application discloses a road friction estimation method and device combined with vehicle platoon and extended Kalman filter, and the method comprises the following steps: designing state equation and measurement equation of road friction estimation; all the vehicles in the vehicle platoon except the tail vehicle send the estimation information of the road adhesion coefficient to the following vehicle through vehicle-to-vehicle communication; all the vehicles in the vehicle platoon except the head vehicle determine whether to use the estimation information of the front vehicle according to the estimation information of the road adhesion coefficient; all the vehicles in the vehicle platoon estimate the current road adhesion coefficient by using the extended Kalman filter, and the estimation process is to solve the state equation and the measurement equation based on the estimation information of the road adhesion coefficient at the last moment. The application designs the extended Kalman filter with a judgment module, and the extended Kalman filters of the platoon members are combined through vehicle-to-vehicle communication of the vehicle platoon, so that the estimation speed and precision of the following vehicle for the road adhesion coefficient are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle platooning, in particular to a road friction estimation method and device for vehicle platooning combined with extended Kalman filtering. BACKGROUND

[0002] With the increase of vehicle ownership, road congestion is becoming more and more serious, and vehicle platooning technology is one of the methods to solve this problem. The collision-free driving requirement of vehicle platooning not only requires the platoon and other vehicles to avoid collision, but also requires the members of the platoon to maintain a safe distance to avoid collision. Under the condition of stable road surface environment and smooth communication, the interval distance between the members of the vehicle platoon can be kept consistent because they experience the same road conditions. However, once the road conditions change, especially when the platoon is long, vehicles at different positions may encounter different road conditions. This requires the platoon to quickly respond to road changes and adjust the inter-vehicle distance in time to ensure the safe driving of the entire platoon. In addition, the safe distance between the members of the vehicle platoon is directly related to the braking distance of each vehicle, and the braking distance is directly affected by the road adhesion coefficient. Therefore, fast and accurate estimation of the road adhesion coefficient is crucial for adjusting the safe distance between the members of the platoon. However, the current road adhesion coefficient state estimator, such as extended Kalman filter or unscented Kalman filter, used in a single vehicle has a slow estimation speed, which cannot guarantee the safety of the vehicle platoon in places where the road adhesion coefficient changes. By improving the estimation method of the road adhesion coefficient, the driving safety and efficiency of the vehicle platoon can be significantly improved, especially under varying road conditions. SUMMARY

[0003] The present application aims to solve the problems in the prior art.

[0004] The technical solution adopted by the present application to solve its technical problems is to provide a road friction estimation method for vehicle platooning combined with extended Kalman filtering, comprising the following steps:

[0005] designing a state equation and a measurement equation for road friction estimation;

[0006] All vehicles in the vehicle platoon except the tail vehicle transmit their estimation information of the road adhesion coefficient to their following vehicles through vehicle-to-vehicle communication; all vehicles in the vehicle platoon except the head vehicle decide whether to use the estimation information of the front vehicle based on their estimation information of the road adhesion coefficient;

[0007] All vehicles in the vehicle platoon estimate the current road adhesion coefficient using an extended Kalman filter, and the estimation process is to solve the state equation and the measurement equation based on the estimation information of the road adhesion coefficient at the previous time.

[0008] Preferably, the state equation and measurement equation for the design road friction estimation include:

[0009] The system state equations for each vehicle are constructed using a 7-DOF vehicle model, and are expressed as follows:

[0010]

[0011] Among them, a x a y These are the longitudinal acceleration and the lateral acceleration, respectively, where m is the vehicle mass; F x and F y These represent the longitudinal and lateral forces on the tires, respectively. The subscripts fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; δ is the front wheel steering angle, and r is the yaw rate. It is yaw acceleration; I z Let l be the moment of inertia of the vehicle about the z-axis. f and l r b represents the distance from the vehicle's center of gravity to the front and rear axles, respectively. f and b r These are the front track and the rear track, respectively.

[0012] The measurement equations of the system are constructed based on the system state equations and are expressed as follows:

[0013] y(t) = h(x(t), u(t), v(t));

[0014] Wherein, the state variable x(t) = [μ fl μ fr μ rl μ rr ] T Including the road surface adhesion coefficient μ fl μ fr μ rl μ rr Input variables and All are tire normalized forces; v(t) is the measurement noise; the measurement quantity is [a x a y r] T .

[0015] Preferably, the process of estimating the road surface adhesion coefficient using the extended Kalman filter includes the following steps:

[0016] The correction module calculates the first state estimate and updates the error matrix;

[0017] The judgment module combines the first state estimate output by the vehicle's own correction module and the first state estimate output by the front vehicle correction module to calculate the second state estimate;

[0018] The prediction module calculates the current predicted state variable and error matrix based on the first state estimation, the second state estimation and the update error matrix;

[0019] The correction module calculates the first state estimation and the update error matrix, the input data for the first calculation is the initial condition, and the initial condition is an initial condition of a road adhesion coefficient manually set; the input for the subsequent calculation is the current predicted state variable and error matrix output by the prediction module and the current measurement value.

[0020] Preferably, the second state estimation is calculated and represented as:

[0021]

[0022] Wherein, represents the second state estimation output by the judgment module to the prediction module; x(t) represents the first state estimation output by the correction module; n+1 represents that the current car is the n+1th car in the vehicle formation, and n represents that the preceding car followed by the current car is the n+1th car in the vehicle formation; Y represents a logical value given by the judgment module, Y=1 indicates that the current car enters a new road, and Y=0 indicates that the current car has not entered a new road.

[0023] Preferably, the assignment of the logical value Y is specifically:

[0024] If the product of (x(t+3)-x(t+2)) / T and (x(t+2)-x(t+1)) / T is greater than zero, Y=1; otherwise, Y=0.

[0025] Preferably, the assignment of the logical value Y is specifically:

[0026] When (x(t+3)-x(t+2)) / T>k and (x(t+2)-x(t+1)) / T>k are established, and (x(t+1)-x(t)) / T>k is not established, Y=1; T represents a time interval;

[0027] In other cases, Y=0.

[0028] Preferably, the vehicle-to-vehicle communication of the vehicle formation is only between adjacent front and rear cars, and the front car only sends information to the rear car, and the rear car only receives information from the front car.

[0029] The application also provides a road friction estimation method combining a vehicle formation and an extended Kalman filter, comprising:

[0030] An equation establishment module is configured to design state equations and measurement equations of the road friction estimation system;

[0031] An information communication module, all vehicles in the vehicle platoon except the tail vehicle, send the estimation information of the road adhesion coefficient to the following vehicle through vehicle-to-vehicle communication; all vehicles in the vehicle platoon except the head vehicle, decide whether to receive the estimation information of the front vehicle according to the estimation information of the road adhesion coefficient.

[0032] A friction estimation module, the extended Kalman filter of all vehicles in the vehicle platoon solves the state equation and the measurement equation by using the estimation information of the road adhesion coefficient at the last moment to estimate the current road adhesion coefficient.

[0033] The present application has the following beneficial effects: the road adhesion coefficient estimation method based on the vehicle platoon joint extended Kalman filter provided by the present application uses the extended Kalman filter added with a judgment module in the road condition with changing road adhesion coefficient, cooperates with the vehicle-to-vehicle communication of the vehicle platoon, accelerates the estimation speed of the following vehicle on the new road adhesion coefficient, improves the estimation accuracy of the following vehicle compared with the traditional single vehicle estimation mode, and provides more accurate information for the longitudinal distance control of the vehicle platoon.

[0034] The present application will be further described in detail below in combination with the drawings and embodiments, but the present application is not limited to the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The method step diagram of the embodiment of the present application;

[0036] Figure 2 The extended Kalman filter structure diagram of the embodiment of the present application;

[0037] Figure 3 The vehicle-to-vehicle communication topology diagram of the embodiment of the present application;

[0038] Figure 4 The device structure diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0039] Referring to Figure 1 The method step diagram of the embodiment of the present application is shown, including the following steps:

[0040] S101, the state equation and the measurement equation of the road friction estimation are designed;

[0041] S102, all vehicles in the vehicle platoon except the tail vehicle send the estimation information of the road adhesion coefficient to the following vehicle through vehicle-to-vehicle communication; all vehicles in the vehicle platoon except the head vehicle decide whether to use the estimation information of the front vehicle according to the estimation information of the road adhesion coefficient.

[0042] S103, all vehicles in the vehicle formation use an extended Kalman filter to estimate the current road surface adhesion coefficient. The estimation process involves solving the state equation and measurement equation based on the estimated road surface adhesion coefficient information from the previous moment.

[0043] Specifically, the process of estimating the road surface adhesion coefficient using the extended Kalman filter is as follows: Figure 2 As shown, it includes the following steps:

[0044] S1031, The correction module calculates the first state estimate and updates the error matrix;

[0045] S1032, The judgment module combines the first state estimate output by the vehicle's own correction module and the first state estimate output by the front vehicle correction module to calculate the second state estimate;

[0046] S1033, the prediction module calculates the current predicted state variables and error matrix based on the first state estimate, the second state estimate and the updated error matrix;

[0047] Specifically, the correction module calculates the first state estimate and updates the error matrix. The input data for the first calculation is the initial conditions, which are the initial conditions of the road surface adhesion coefficient set manually. The inputs for subsequent calculations are the current predicted state variables and error matrix output by the prediction module, as well as the current measurement values.

[0048] Specifically, see Figure 3 The diagram shown illustrates the communication topology of an embodiment of the present invention. Vehicle-to-vehicle communication within a platoon occurs only between adjacent vehicles; the leading vehicle sends information to the following vehicle, and the following vehicle receives information from the leading vehicle. Furthermore, the extended Kalman filter of the first vehicle in the platoon (the lead vehicle) does not require a judgment module. The result of the leading vehicle's correction module is transmitted to the judgment module of the extended Kalman filter of the following vehicles via vehicle-to-vehicle communication within the platoon. The judgment module of the extended Kalman filter of the following vehicles determines whether to enter a new road surface based on its own estimation of the road surface adhesion coefficient.

[0049] Specifically, the judgment result of the judgment module is to assign a value to the logic value Y. In order to prevent the logic value from being 1 due to the estimation fluctuation of the extended Kalman filter, the product of (x(t+3)-x(t+2)) / T and (x(t+2)-x(t+1)) / T needs to be greater than zero, so as to avoid the fluctuation of the extended Kalman filter when entering a new road surface causing the condition to be satisfied.

[0050] The assignment logic of this invention embodiment is shown in Table 1, which is the logic table of the judgment module.

[0051] Table 1

[0052]

[0053] From Table 1, when (x(t+3)-x(t+2)) / T>k and (x(t+2)-x(t+1)) / T>k are true, and (x(t+1)-x(t)) / T>k is not true, Y=1; T represents a time interval; otherwise, Y=0; k is a set threshold, k≠0.

[0054] When the estimation result of the vehicle itself satisfies Y=1, it is determined that the vehicle enters a new road surface, at this time, the estimation result of the new road surface estimated by the preceding vehicle is received and used to update the estimation result of the vehicle itself, and the judgment condition of the table is used, that is, the following vehicle only receives information once. Specifically as follows:

[0055]

[0056] The upper left corner n represents the vehicle number, represents the input of the extended Kalman filter prediction module, Y represents the logic value of the extended Kalman filter judgment module, when the change rate between two consecutive estimation values exceeds the set threshold, that is, 1 is assigned, otherwise 0.

[0057] Specifically, the S101 includes the following steps:

[0058] A 7-DOF vehicle model is used to construct the system state equation of each vehicle, which is represented as:

[0059]

[0060]

[0061] Wherein, a x , a y are longitudinal acceleration and lateral acceleration, m is vehicle mass, F x , F y are longitudinal force and lateral force of the tire, fl, fr, rl, rr are left front, right front, left rear, and right rear wheels, δ is the front wheel steering angle, r is the yaw rate, I z is the moment of inertia of the vehicle around the z-axis, l f , l r are the distances from the vehicle mass center to the front and rear axles, b f , b r are the front and rear wheel tracks.

[0062] The measurement equation of the system is constructed, which is represented as:

[0063] y(t)=h(x(t),u(t),v(t));

[0064] Wherein, the measurement y(t)=[a x a y r]T ; input variable is the normalized force of the tire; state variable x(t) = [μ fl μ fr μ rl μ rr ] T ; v(t) is the measurement noise;

[0065] The road surface adhesion coefficient μ fl , μ fr , μ rl , μ rr .

[0066] Referring to Figure 4 , it is a system structure diagram of an embodiment of the present application, comprising:

[0067] The equation establishment module 401 designs the state equation and the measurement equation of the road friction estimation system;

[0068] The information communication module 402, all vehicles in the vehicle platoon except the tail vehicle, send their estimation information of the road surface adhesion coefficient to their following vehicles through vehicle-to-vehicle communication; all vehicles in the vehicle platoon except the head vehicle, decide whether to receive the estimation information of the front vehicle according to their estimation information of the road surface adhesion coefficient;

[0069] The friction estimation module 403, the extended Kalman filter of all vehicles in the vehicle platoon solves the state equation and the measurement equation by using the estimation information of the road surface adhesion coefficient at the last moment, to estimate the current road surface adhesion coefficient.

[0070] The road surface adhesion coefficient estimation method based on the joint extended Kalman filter of the vehicle platoon provided by the present application, in the estimation of the road surface adhesion coefficient of the vehicle platoon, designs the extended Kalman filter with a judgment module, and then uses the vehicle-to-vehicle communication mode of the vehicle platoon to combine the extended Kalman filters of the platoon members, this method not only can accelerate the estimation of the new road surface adhesion coefficient of the following vehicles of the vehicle platoon, but also can improve the estimation accuracy of the following vehicles. In addition to the traditional single estimation of a single vehicle, this method does not need to additionally install sensors, saves the cost, and also provides more accurate information for the longitudinal distance control of the vehicle platoon.

[0071] The above is only a preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A road friction estimation method for vehicle platooning incorporating extended Kalman filtering, characterized by, The method comprises the following steps: designing state equation and measurement equation of road friction estimation; all vehicles in the vehicle platoon except the tail vehicle send their estimation information of the road adhesion coefficient to their following vehicles through vehicle-to-vehicle communication; all vehicles in the vehicle platoon except the head vehicle decide whether to use the estimation information of the front vehicle according to their estimation information of the road adhesion coefficient; all vehicles in the vehicle platoon estimate the current road adhesion coefficient by using an extended Kalman filter, and the estimation process is solving the state equation and the measurement equation based on the estimation information of the road adhesion coefficient at the previous moment; the state equation and the measurement equation of road friction estimation are designed, comprising: ； ； ； ； wherein , are the longitudinal and lateral accelerations, respectively, is the vehicle mass; and are the longitudinal and lateral forces of the tires, respectively, and the subscript i = 1, 2, 3, 4 denotes the front left wheel, the front right wheel, the rear left wheel and the rear right wheel, respectively; , , and denote the front left wheel, the front right wheel, the rear left wheel and the rear right wheel, respectively; is the front wheel steering angle, is the yaw acceleration; is the moment of inertia of the vehicle about the z-axis, and are the distances of the vehicle center of mass from the front and rear axles, respectively, and are the front and rear wheel tracks, respectively. a 7-DOF vehicle model is used to construct the system state equation of each vehicle, which is expressed as: ； where the state variables , including the road adhesion coefficient , , , ; the input variables , , and are the tire normalized forces; is the measurement noise; the measurement is ; a measurement equation of the system is constructed based on the system state equation, which is expressed as: the process of the extended Kalman filter estimating the road adhesion coefficient comprises the following steps: a correction module calculates a first state estimation and an update error matrix; a judgment module calculates a second state estimation by combining the first state estimation output by the vehicle's own correction module and the first state estimation output by the front vehicle's correction module; a prediction module calculates a current predicted state variable and an error matrix based on the first state estimation, the second state estimation and the update error matrix; 2. The method of estimating road friction using extended Kalman filter in vehicle platooning according to claim 1, wherein, the correction module calculates the first state estimation and the update error matrix, and the input data for the first calculation is an initial condition, and the initial condition is an initial condition of the road adhesion coefficient manually set; the input for the subsequent calculation is the current predicted state variable and the error matrix output by the prediction module and the current measurement value. ； wherein, represents the second state estimation outputted by the judging module to the predicting module; represents the first state estimation outputted by the correcting module; n+1 represents that the current car is the n+1th car in the vehicle platoon, n represents that the preceding car followed by the current car is the n+1th car in the vehicle platoon; Y represents the logic value given by the judging module, Y=1 indicates that the current car enters a new road, and Y=0 indicates that the current car has not entered a new road.

3. The method of estimating road friction using extended Kalman filter in vehicle platooning according to claim 2, wherein, the calculation of the second state estimation is expressed as: the assignment of the logical value Y is specifically:

4. The method of estimating road friction using extended Kalman filter in vehicle platooning according to claim 2, wherein, if the product of (x(t+3)-x(t+2)) / T1 and (x(t+2)-x(t+1)) / T1 is greater than zero, Y=1; otherwise, Y=0; T1 represents a time interval. the assignment of the logical value Y is specifically: when (x(t+3)-x(t+2)) / T1>k and (x(t+2)-x(t+1)) / T1>k are true, and (x(t+1)-x(t)) / T1>k is not true, Y=1; k is a set threshold value; 5. The method of estimating road friction using extended Kalman filter in vehicle platooning according to claim 1, wherein, otherwise, Y=0.

6. A device for estimating road friction in combination with extended Kalman filtering for vehicle platooning, characterized by, The vehicle-to-vehicle communication of the vehicle platoon is only between adjacent front and rear vehicles, and the front vehicle only sends information to the rear vehicle, and the rear vehicle only receives the information of the front vehicle. comprising: an equation establishing module for designing the state equation and the measurement equation of the road friction estimation system; an information communication module, all vehicles in the vehicle platoon except the tail vehicle send their estimation information of the road adhesion coefficient to their following vehicles through vehicle-to-vehicle communication; all vehicles in the vehicle platoon except the head vehicle decide whether to receive the estimation information of the front vehicle according to their estimation information of the road adhesion coefficient; a friction estimation module, the extended Kalman filter of all vehicles in the vehicle platoon solves the state equation and the measurement equation based on the estimation information of the road adhesion coefficient at the previous moment to estimate the current road adhesion coefficient; the state equation and the measurement equation of road friction estimation are designed, comprising: a 7-DOF vehicle model is used to construct the system state equation of each vehicle, which is expressed as: ； ； ； ； wherein , are the longitudinal and lateral accelerations, respectively, is the vehicle mass; and are the longitudinal and lateral forces of the tires, respectively, and the subscript , , and denote the front left wheel, the front right wheel, the rear left wheel and the rear right wheel, respectively; is the front wheel steering angle, is the yaw acceleration; is the moment of inertia of the vehicle about the z-axis, and are the distances of the vehicle center of mass from the front and rear axles, respectively, and are the front and rear wheel tracks, respectively. A measurement equation of the system is constructed based on a system state equation, and is expressed as: ； where the state variables , including the road adhesion coefficient , , , ; the input variables , , and are the tire normalized forces; is the measurement noise; the measurement is ; The process of estimating the road adhesion coefficient by the extended Kalman filter comprises the following steps: The correction module calculates the first state estimation and the update error matrix; The judgment module combines the first state estimation output by the vehicle self-correction module and the first state estimation output by the preceding vehicle correction module to calculate a second state estimation; The prediction module calculates a current predicted state variable and an error matrix based on the first state estimation, the second state estimation and the update error matrix; The correction module calculates the first state estimation and the update error matrix, and the input data for the first calculation is an initial condition, and the initial condition is an initial condition of the road adhesion coefficient manually set; the input for the subsequent calculation is the current predicted state variable and the error matrix output by the prediction module and the current measurement value.

Images