CN112572436A - Vehicle following control method and system - Google Patents
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Abstract
The invention relates to a vehicle following control method and a system, wherein the method comprises the following steps: acquiring a motion state sequence of a front vehicle; determining the tracking distance error between the controlled vehicle and the front vehicle and the speed error between the controlled vehicle and the front vehicle according to the motion state sequence of the front vehicle; determining a longitudinal target acceleration of a controlled vehicle according to a tracking distance error between the controlled vehicle and a front vehicle, a speed error between the controlled vehicle and the front vehicle and a longitudinal acceleration of the controlled vehicle; determining a tracking target point of a controlled vehicle according to the motion state sequence of the front vehicle; establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point; determining an error model of the controlled vehicle according to the lateral dynamics equation of the controlled vehicle; and determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle. The invention reduces the influence of communication delay and packet loss and improves the accuracy of vehicle following.
Description
Technical Field
The invention relates to the technical field of vehicle tracking, in particular to a vehicle following control method and system.
Background
With the development of vehicle-mounted technologies, including communication technologies, sensor technologies, control technologies, and the like, intelligent networked automobiles draw extensive attention in the automobile industry. In the communication environment of the Internet of vehicles, the intelligent Internet-connected automobile can acquire more reference information from other Internet-connected automobiles and the fixed base station, so that the stability, the energy conservation and the traffic performance of the intelligent Internet-connected automobile are improved.
The track tracking control is one of key technologies for realizing automatic driving of the intelligent vehicle, and aims to generate vehicle real-time control quantities such as front wheel turning angles, driving force/braking force and the like according to a track planned by an upper-layer controller and real-time state information of the vehicle so as to ensure that the vehicle can run according to an expected track. Common control algorithms include PID (proportional Integral Differential), LQR (linear quadratic regulator), sliding mode, pure tracking control, and the like. However, the above method generally has a problem of high degree of dependence on parameters and environment, and once the environment changes, the tracking control effect is often deteriorated.
Description of prior art solutions;
scheme 1: patent CN109978909A proposes an intelligent vehicle tracking method and system thereof. The method comprises the steps of setting a vehicle detection area and a vehicle tracking area; in the vehicle detection area, detecting a vehicle by using a direction gradient Histogram (HOG) detection mode and a Support Vector Machine (SVM) detection mode; in the vehicle tracking area, tracking the detected vehicle by using an image pyramid optical flow tracking mode to generate a tracking track of the vehicle; in the invention, in a vehicle tracking area, an image pyramid optical flow tracking mode is used for tracking a detected vehicle, and a tracking track of the vehicle is generated.
Scheme 2: patent CN106228805B proposes an interactive multi-vehicle tracking method and device. The basic principle is as follows: s1, establishing a multi-vehicle interactive vehicle tracking model; s2, respectively acquiring measurement information of each vehicle to be tracked at the kth moment by using a sensor; wherein, one sensor corresponds to one vehicle to be tracked; and S3, estimating the motion state parameters of the vehicle to be tracked by using a non-dimension-expanding method according to the measurement information acquired in the step S2 and the vehicle tracking model established in the step S1, so as to realize the tracking of the vehicle to be tracked.
Scheme 3: patent CN103871079B proposes a vehicle tracking method based on machine learning and optical flow. The basic principle is as follows: the method comprises the steps of obtaining a vehicle model through one-time off-line training, detecting vehicle block masses Blob in real time in a video stream by using the vehicle model, calculating a feature point set of each vehicle block mass Blob, carrying out bidirectional pyramid optical flow tracking, analyzing and filtering forward and backward optical flow tracking results, realizing stable and accurate tracking of multiple targets, forming a vehicle track, and realizing stable and accurate tracking of the multiple targets, such as long-term vehicle stopping, scale change, shadow, local shielding, adhesion and the like in a scene; especially, the method has better results for bad weather, low illumination and high noise.
Scheme 4: patent CN109979222A proposes an intelligent vehicle tracking and scheduling method and system. The basic principle is as follows: acquiring task distribution information; calling road coordinate information of a map information module; calculating to obtain optimal distribution path information through a self-adaptive ant colony algorithm; sending the optimal distribution path information to a terminal node through a Zigbee coordination controller; the method comprises the steps of acquiring address information of a starting point and address information of an end point of task distribution, and calling road coordinate information of a map information module; then calculating to obtain the optimal distribution path information from the starting point to the end point of the distribution task through a self-adaptive ant colony algorithm; and finally, the optimal distribution path information is sent to the terminal nodes through the Zigbee coordination controller, and vehicles carrying the terminal nodes carry out distribution tasks according to the optimal distribution paths.
For scheme 1: the method comprises the steps that a pure visual sensor obtains image information of a front vehicle, a direction gradient histogram HOG detection mode and a support vector machine SVM are used for detecting the vehicle, and an image pyramid optical flow tracking mode is used for tracking the detected vehicle. In a short-distance following state, it is difficult to acquire sufficient preceding vehicle information by means of only a vision sensor. In addition, the tracking mode based on the visual sensor does not depend on a vehicle dynamic model, the global reliability of vehicle tracking is difficult to ensure, and the out-of-control condition is easy to occur.
For scheme 2: the interactive vehicle tracking control method proposed by scheme 2 regards the information interaction process as an ideal process, and ignores non-ideal factors existing in the communication system, such as information delay, packet loss and the like.
For scheme 3: in the scheme 3, vehicle tracking control is realized by combining machine learning and a vision sensor, but a model obtained by machine learning is difficult to explain by using a physical model, and high robustness of a vehicle control system under different environments cannot be ensured.
For scheme 4: scheme 4 is based on the upper control system to send the target path to the vehicle, and the calculation capability requirement on the control system is complex, so that the method is only suitable for controlling special vehicles in a fixed environment or a closed road, and is not suitable for tracking control of vehicles in a conventional traffic environment.
Disclosure of Invention
Based on this, the invention aims to provide a vehicle following control method and a vehicle following control system, which reduce the influence of communication delay and packet loss and improve the accuracy of vehicle following.
In order to achieve the purpose, the invention provides the following scheme:
a vehicle following control method, the method comprising:
acquiring a motion state sequence of a front vehicle;
determining the tracking distance error between the controlled vehicle and the front vehicle and the speed error between the controlled vehicle and the front vehicle according to the motion state sequence of the front vehicle;
determining a longitudinal target acceleration of a controlled vehicle according to a tracking distance error between the controlled vehicle and a front vehicle, a speed error between the controlled vehicle and the front vehicle and a longitudinal acceleration of the controlled vehicle;
determining a tracking target point of a controlled vehicle according to the motion state sequence of the front vehicle;
establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point;
determining an error model of the controlled vehicle according to the lateral dynamics equation of the controlled vehicle;
and determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle.
Optionally, the acquiring the motion state sequence of the leading vehicle specifically includes:
and acquiring the motion state sequence of the front vehicle through V2X communication.
Optionally, after determining the longitudinal target acceleration of the controlled vehicle according to the tracking distance error of the controlled vehicle and the front vehicle, the speed error of the controlled vehicle and the front vehicle, and the longitudinal acceleration of the controlled vehicle, the method further includes:
converting the longitudinal target acceleration of the controlled vehicle into a longitudinal required torque;
and performing longitudinal control on the controlled vehicle according to the longitudinal demand torque.
Optionally, the determining a tracking target point of the controlled vehicle according to the motion state sequence of the leading vehicle specifically includes:
obtaining the yaw velocity, the course angle and the transverse displacement of the front vehicle according to the motion state sequence of the front vehicle;
and determining a tracking target point of the controlled vehicle according to the yaw velocity, the course angle and the transverse displacement of the front vehicle.
Optionally, the determining an error model of the controlled vehicle according to the lateral dynamics equation of the controlled vehicle specifically includes:
performing linear transformation on the lateral dynamic equation of the controlled vehicle to obtain a linearized form of the lateral dynamic equation of the controlled vehicle;
and subtracting the lateral dynamic equation of the controlled vehicle from the linearized form of the lateral dynamic equation of the controlled vehicle to determine an error model of the controlled vehicle.
Optionally, the determining a steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle specifically includes:
adopting a positive Euler method to convert an error model of the controlled vehicle to obtain a system state equation of the controlled vehicle;
establishing an optimized objective function of a system state equation of the controlled vehicle according to the deviation of the minimum predicted output path point and the tracking target point;
and determining the steering wheel angle of the controlled vehicle according to the system state equation of the controlled vehicle by taking the optimization objective function as a constraint.
The invention also provides a vehicle following control system, which comprises:
the motion state sequence acquisition module of the front vehicle is used for acquiring the motion state sequence of the front vehicle;
the front vehicle and controlled vehicle error data determining module is used for determining the tracking distance error between the controlled vehicle and the front vehicle and the speed error between the controlled vehicle and the front vehicle according to the motion state sequence of the front vehicle;
the longitudinal target acceleration determining module is used for determining the longitudinal target acceleration of the controlled vehicle according to the tracking distance error between the controlled vehicle and the front vehicle, the speed error between the controlled vehicle and the front vehicle and the longitudinal acceleration of the controlled vehicle;
the tracking target point determining module is used for determining a tracking target point of the controlled vehicle according to the motion state sequence of the front vehicle;
the lateral dynamics equation determining module is used for establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point;
the error model determination module of the controlled vehicle is used for determining an error model of the controlled vehicle according to a lateral dynamic equation of the controlled vehicle;
and the steering wheel angle determining module is used for determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle.
Optionally, the motion state sequence obtaining module of the leading vehicle specifically includes:
and the motion state sequence acquisition unit of the front vehicle is used for acquiring the motion state sequence of the front vehicle through V2X communication.
Optionally, the system further comprises:
the longitudinal target acceleration conversion module is used for converting the longitudinal target acceleration of the controlled vehicle into longitudinal required torque;
and the longitudinal control module is used for longitudinally controlling the controlled vehicle according to the longitudinal required torque.
Optionally, the tracking target point determining module specifically includes:
the front vehicle motion data extraction unit is used for obtaining the yaw velocity, the course angle and the transverse displacement of the front vehicle according to the motion state sequence of the front vehicle;
and the tracking target point determining unit is used for determining the tracking target point of the controlled vehicle according to the yaw velocity, the course angle and the transverse displacement of the front vehicle.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a vehicle following control method and a vehicle following control system, wherein data in a motion state sequence of a front vehicle are acquired, and the longitudinal target acceleration of a controlled vehicle is determined according to the tracking distance error of the controlled vehicle and the front vehicle, the speed error of the controlled vehicle and the front vehicle and the longitudinal acceleration of the controlled vehicle; determining a tracking target point of a controlled vehicle, establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point, determining an error model of the controlled vehicle according to the lateral dynamics equation of the controlled vehicle, and determining a steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle, so that the controlled vehicle is longitudinally controlled according to a longitudinal target acceleration of the controlled vehicle, and the controlled vehicle is transversely controlled according to the steering wheel angle of the controlled vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a vehicle following control method according to the present invention;
FIG. 2 is a communication packet loss model according to the present invention;
FIG. 3 is a schematic diagram of an overall control architecture of a vehicle following control method according to the present invention;
FIG. 4 is a schematic diagram of the route information sent by the leading vehicle according to the present invention;
FIG. 5 is a schematic view of a model of the movement of a controlled vehicle according to the present invention;
fig. 6 is a schematic structural diagram of a vehicle following control system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a vehicle following control method and a vehicle following control system, which reduce the influence of communication delay and packet loss and improve the accuracy of vehicle following.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a vehicle following control method according to the present invention, and as shown in fig. 1, the vehicle following control method includes:
step 101: and acquiring a motion state sequence of the front vehicle.
Wherein, step 101 specifically includes:
and acquiring the motion state sequence of the front vehicle through V2X communication.
The invention considers the phenomena of information delay and packet loss in the V2X communication system.
In a V2X communication environment, whether a cellular vehicular network or a dedicated short range communication standard, the sampling time is typically 100 ms. The communication delay is random and is determined by factors such as vehicle speed, communication bandwidth and node number. In V2X communication, the communication delay is generally a time-varying variable of 100ms or less. Thus, the communication delay is expressed as: β (t) ═ β ≦ ζ.
Where β is the general communication delay, ζ is the maximum communication delay, and t represents the real time.
Packet loss during communication is considered random and unpredictable. In order to adequately reflect the stochastic nature of packet loss, the present invention employs a bernoulli model that describes the process of information loss. Bernoulli models can model the behavior of an actual communication channel well, where the probability of transmission of a data packet is typically dependent on the current channel state. As shown in fig. 2, a state "1" indicates a successful communication state, and a state "0" indicates an exit state. The transition probability from state 1 to state 0 is q and the transition probability from state 0 to state 1 is p. The transition probability of the bernoulli model in the simulation is set by a random number.
Step 102: and determining the tracking distance error between the controlled vehicle and the front vehicle and the speed error between the controlled vehicle and the front vehicle according to the motion state sequence of the front vehicle. The front vehicle is a vehicle to be tracked by the controlled vehicle.
Step 103: and determining the longitudinal target acceleration of the controlled vehicle according to the tracking distance error of the controlled vehicle and the front vehicle, the speed error of the controlled vehicle and the front vehicle and the longitudinal acceleration of the controlled vehicle.
Wherein, step 103 specifically comprises:
the longitudinal dynamics system of the controlled vehicle is represented as:
where τ is the time constant, φ is the actuator delay, kGAnd (b) is a steady-state gain, X(s) is a system state, U(s) is a system input, specifically a frequency domain longitudinal target acceleration of the controlled vehicle, G(s) is a transfer function of the system, and s is an operator.
The target distance between the controlled vehicle and the front vehicle is a dynamically changing value and is represented as:
ddes＝d0+hv；
where h is the time interval between the controlled vehicle and the front vehicle, v is the speed of the controlled vehicle, and ddesIs the target distance between the controlled vehicle and the preceding vehicle, d0Is the static target distance between the controlled vehicle and the front vehicle.
The tracking error of the controlled vehicle and the front vehicle is represented as: e.g. of the typed＝dr-ddes＝dr-d0-hv。
The speed error of the controlled vehicle and the front vehicle is represented as: v. ofr＝vp-v。
In the formula, edTo track distance errors, vrAs a speed error, drFor the real distance between the controlled vehicle and the preceding vehicle, vpThe vehicle speed of the front vehicle.
The longitudinal tracking target is represented as:
wherein, axIs the time-domain longitudinal target acceleration of the controlled vehicle, axpIs the front vehicle longitudinal acceleration.
The control input for longitudinal control of the controlled vehicle includes two feedback terms and a feedforward term, expressed as:
U(s)＝kded+kvvr+D(s)F(s)axp；
wherein k isdedAnd kvvrAs a feedback term, kdAnd kvAre feedback factors, D(s) F(s) axpFor the feed-forward term, D(s) ═ e-ζsF(s) is a feedforward compensation filter, and F(s) is expressed as:
wherein, after step 103, the method further comprises:
and converting the longitudinal target acceleration of the controlled vehicle into the longitudinal required torque.
And performing longitudinal control on the controlled vehicle according to the longitudinal demand torque.
The input of the longitudinal control of the controlled vehicle is longitudinalTarget acceleration axThe longitudinal target acceleration may be converted into a longitudinal required torque expressed as:
wherein, TallFor total required torque (longitudinal required torque), f is the rolling resistance coefficient, IwIn order to control the moment of inertia of the wheels of the vehicle,
angular acceleration of the wheels of the vehicle to be controlled, CdIs the wind resistance coefficient, AdIs the frontal area, etamFor motor efficiency, m is the service mass of the controlled vehicle.
Step 104: and determining a tracking target point of the controlled vehicle according to the motion state sequence of the front vehicle.
Wherein, step 104 specifically includes:
and acquiring the yaw velocity, the course angle and the transverse displacement of the front vehicle according to the motion state sequence of the front vehicle.
And determining a tracking target point of the controlled vehicle according to the yaw velocity, the course angle and the transverse displacement of the front vehicle.
Acquiring a motion state information sequence of the front vehicle within 1s through V2X communication, as shown in FIG. 4, A in FIG. 4 represents the route and vehicle information sent by the front vehicle, B represents the route, based on the positioning information of the controlled vehicle, acquiring a route point in the front vehicle information sequence which is far away from the current position of the controlled vehicle as a tracking target point,
wherein eta isref(t + i | t) represents the ith path point of the preceding vehicle, i.e., the ith tracking target point of the controlled vehicle,
indicating the yaw rate, ψ, of the preceding vehicle at the ith waypointref(t + i | t) represents the time before the ith waypointCourse angle of vehicle, YrefThe (t + i | t) represents the displacement in the Y direction (lateral direction) in the geodetic coordinate system at the ith route point, and the preceding vehicle position information is position information in the geodetic coordinate system and needs to be converted into position information in the coordinate system of the controlled vehicle by coordinate conversion. The conversion equation is as follows:
wherein Y isrefvehIs the lateral displacement of the front vehicle relative to the controlled vehicle under the coordinate system of the controlled vehicle, (X)ref,Yref) The position coordinates of the front vehicle in the geodetic coordinate system are shown, (X, Y) the position coordinates of the controlled vehicle in the geodetic coordinate system are shown, and psi the heading angle of the controlled vehicle is shown.
Therefore, the i-th tracking target point of the controlled vehicle after the coordinate conversion is represented as:
step 105: and establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point.
Wherein the lateral dynamics equation of the controlled vehicle in step 105 is expressed as:
wherein, the controlled vehicle model is shown in fig. 5, X and Y are coordinates of the vehicle under the controlled vehicle coordinate system, X and Y are coordinates of the controlled vehicle under the earth coordinate system, and Fijy(i-R, F, j-R, L) is the lateral force of the four wheels, Fijx(i ═ R, F, j ═ R, L) is the longitudinal force of the four wheels, αijy(i R, F, j R, L) is the slip angle of the four wheels, δ is the front wheel angle, a and B are the distance from the center of mass to the front and rear axle, respectively, BfAnd BrRespectively, the wheel track of the front axle and the wheel track of the rear axle, m is the controlled vehicle servicing mass, IzIs at the center of mass of the controlled vehicleIs the heading angle of the controlled vehicle. In the formula, one point on a variable represents the first derivative of the variable, and two points on the variable represent the second derivative of the variable.
Step 106: and determining an error model of the controlled vehicle according to the lateral dynamic equation of the controlled vehicle.
Step 107: and determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle.
Wherein, step 107 specifically comprises:
and converting the error model of the controlled vehicle by adopting a positive Euler method to obtain a system state equation of the controlled vehicle.
And establishing an optimized objective function of a system state equation of the controlled vehicle by minimizing the deviation of the predicted output path point and the tracking target point.
And determining the steering wheel angle of the controlled vehicle according to the system state equation of the controlled vehicle by taking the optimization objective function as a constraint.
The step 106-107 specifically comprises:
and performing linear transformation on the lateral dynamic equation of the controlled vehicle to obtain a linearized form of the lateral dynamic equation of the controlled vehicle.
And subtracting the lateral dynamic equation of the controlled vehicle from the linearized form of the lateral dynamic equation of the controlled vehicle to determine an error model of the controlled vehicle.
The lateral dynamic state equation of the controlled vehicle can be expressed as:
where ξ (t) represents the amount of change in ξ with time, u (t) represents the amount of change in the front wheel angle u with time,
subtracting the general form from the linearized form (the general form is the form before the linearization of the lateral dynamics equation), and obtaining a linearized controlled vehicle error model as follows:
and (3) using a positive Euler method, and replacing the differential with a first-order difference quotient to obtain an expression of a lateral dynamic state equation of the controlled vehicle in a discrete state space:
where a ═ a (k) ═ I + ta (T) is a state matrix, B ═ B (k) ═ tb (T) is a control matrix, C ═ C (k) ═ C (T) is an output matrix, I is a unit matrix, T is a controller execution period, ξ is a controller execution period, and ta is a controller execution periodrLast moment variable, u, representing xirThe last time variable of u is represented,
to represent
The first derivative of (a) is,
is representative of xirThe difference from xi is the form of the augmentation,
represents urThe difference from u is the form of the augmentation.
To this end, the lateral dynamic state equation of the controlled vehicle is expressed as:
wherein,
η (k | t) represents the output in the prediction time domain of the system, and Δ u (k | t) represents the control increment of the front wheel rotation angle.
Let k be the current time, NpTo predict the time domain, NcTo control the time domain, and Nc≤Np. The system state in the prediction horizon can be represented as:
the output in the prediction time domain of the system can be expressed as:
the output of the state space can be represented in matrix form:
Y(k)＝Ψ(k)x(k)+Θ(k)ΔU(k)；
wherein,
in order to realize real-time tracking of the reference path of the front vehicle, the optimal control objective is to reduce the deviation of the predicted output and the reference path. Meanwhile, the limitations of actuating mechanisms such as a steering mechanism, a driving motor and the like are also considered. Because the vehicle dynamics model is a time-varying model and a plurality of constraint conditions are introduced, it is difficult to ensure that the optimization target can be solved at every moment. Therefore, a relaxation factor is added to the optimization target, and the solving difficulty is reduced. The optimization objective function of the model predictive control is as follows:
wherein Q∈R3×3And R ∈ R2×2Weight matrix, ε, for control accuracy and control increment>0 is the relaxation factor and ρ is the weight of the relaxation factor. The first half of the optimization objective function represents the effect of reducing the tracking deviation, and the second half represents the effect of reducing the fluctuation of the control variable. The solved control variables are represented in incremental form.
To use SQP (sequence quadratic programming), the optimization objective function is transformed into a quadratic form:
wherein Δ UtIs the control increment, UtTo control the amount, yhcIs the output hard constraint, yscIs output soft constraints, and
incremental control sequence Δ UtAs the actual incremental control variable. The actual control variable (actual steering wheel angle) is derived based on the addition of the actual incremental control variable to the control variable of the previous stage, and can be expressed as:
and controlling the controlled vehicle according to the obtained steering wheel angle and the longitudinal required torque, so as to realize the accurate tracking of the controlled vehicle on the front vehicle.
The overall control architecture of the controlled vehicle is shown in FIG. 3. The controlled vehicle 1 and the front vehicle are communicated through the internet of vehicles, the controlled vehicle 1 obtains a motion state sequence of the front vehicle through the internet of vehicles communication, the motion state sequence of the front vehicle is referred, the longitudinal and transverse coupling control 2 is utilized, the longitudinal controller enables the controlled vehicle 1 to keep a target speed, a target vehicle distance and a target acceleration, and the transverse controller calculates a target steering wheel turning angle of the controlled vehicle 1. The driving torque and the steering wheel torque form an actuating mechanism 3 of the controlled vehicle 1, and the controlled vehicle 1 is controlled through the target driving torque obtained by the longitudinal controller and the target steering wheel angle obtained by the transverse controller, so that the influence caused by information delay and packet loss is reduced.
The invention provides an intelligent networking automobile following control method considering a non-ideal communication environment, which can reduce the interference of delay and packet loss phenomena in the communication environment on the stability of a vehicle through longitudinal multi-target control and transverse model predictive control without using a visual sensor.
According to the invention, a visual sensor is not needed, the vehicle tracking control in a short vehicle following distance can be realized, the vehicle formation control can be extended, the strong calculation force in the vehicle following scheme of the visual sensor is not needed, and the motion state of the front vehicle is only needed to be received and processed through the V2X communication unit. The invention utilizes multi-target feedforward-feedback control and model predictive control, can reduce the influence of communication delay and packet loss on the stability of vehicle tracking control, and realizes accurate tracking of the longitudinal and transverse tracks of the front vehicle.
Fig. 6 is a schematic structural view of a vehicle following control system according to the present invention, and as shown in fig. 6, the present invention further provides a vehicle following control system, which includes:
and a motion state sequence obtaining module 201 of the preceding vehicle, configured to obtain a motion state sequence of the preceding vehicle.
And the front vehicle and controlled vehicle error data determining module 202 is used for determining the tracking distance error and the speed error of the controlled vehicle and the front vehicle according to the motion state sequence of the front vehicle.
And the longitudinal target acceleration determining module 203 is used for determining the longitudinal target acceleration of the controlled vehicle according to the tracking distance error of the controlled vehicle and the front vehicle, the speed error of the controlled vehicle and the front vehicle and the longitudinal acceleration of the controlled vehicle.
And a tracking target point determining module 204, configured to determine a tracking target point of the controlled vehicle according to the motion state sequence of the leading vehicle.
And the lateral dynamics equation determining module 205 is used for establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point.
And the error model determination module 206 of the controlled vehicle is used for determining the error model of the controlled vehicle according to the lateral dynamic equation of the controlled vehicle.
And the steering wheel angle determining module 207 is used for determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle.
The motion state sequence obtaining module 201 of the leading vehicle specifically includes:
and the motion state sequence acquisition unit of the front vehicle is used for acquiring the motion state sequence of the front vehicle through V2X communication.
The system further comprises:
and the longitudinal target acceleration conversion module is used for converting the longitudinal target acceleration of the controlled vehicle into the longitudinal required torque.
And the longitudinal control module is used for longitudinally controlling the controlled vehicle according to the longitudinal required torque.
The tracking target point determining module 204 specifically includes:
and the preceding vehicle motion data extraction unit is used for obtaining the yaw velocity, the course angle and the transverse displacement of the preceding vehicle according to the motion state sequence of the preceding vehicle.
And the tracking target point determining unit is used for determining the tracking target point of the controlled vehicle according to the yaw velocity, the course angle and the transverse displacement of the front vehicle.
The error model determining module 206 of the controlled vehicle specifically includes:
and the lateral dynamic equation linear transformation unit is used for performing linear transformation on the lateral dynamic equation of the controlled vehicle to obtain a linearized form of the lateral dynamic equation of the controlled vehicle.
And the error model determining unit of the controlled vehicle is used for subtracting the lateral dynamic equation of the controlled vehicle from the linearized form of the lateral dynamic equation of the controlled vehicle to determine the error model of the controlled vehicle.
The steering wheel angle determining module 207 specifically includes:
and the system state equation determining unit of the controlled vehicle is used for converting the error model of the controlled vehicle by adopting a positive Euler method to obtain the system state equation of the controlled vehicle.
And the optimization objective function establishing unit is used for establishing an optimization objective function of a system state equation of the controlled vehicle by minimizing the deviation of the predicted output path point and the tracking target point.
And the steering wheel angle determining unit of the controlled vehicle is used for determining the steering wheel angle of the controlled vehicle according to the system state equation of the controlled vehicle by taking the optimization objective function as a constraint.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A vehicle following control method, characterized by comprising:
acquiring a motion state sequence of a front vehicle;
determining the tracking distance error between the controlled vehicle and the front vehicle and the speed error between the controlled vehicle and the front vehicle according to the motion state sequence of the front vehicle;
determining a longitudinal target acceleration of a controlled vehicle according to a tracking distance error between the controlled vehicle and a front vehicle, a speed error between the controlled vehicle and the front vehicle and a longitudinal acceleration of the controlled vehicle;
determining a tracking target point of a controlled vehicle according to the motion state sequence of the front vehicle;
establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point;
determining an error model of the controlled vehicle according to the lateral dynamics equation of the controlled vehicle;
and determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle.
2. The vehicle following control method according to claim 1, wherein the acquiring of the motion state sequence of the preceding vehicle specifically comprises:
and acquiring the motion state sequence of the front vehicle through V2X communication.
3. The vehicle following control method according to claim 1, wherein after determining the longitudinal target acceleration of the controlled vehicle according to the tracking distance error of the controlled vehicle from the leading vehicle, the speed error of the controlled vehicle from the leading vehicle, and the longitudinal acceleration of the controlled vehicle, further comprising:
converting the longitudinal target acceleration of the controlled vehicle into a longitudinal required torque;
and performing longitudinal control on the controlled vehicle according to the longitudinal demand torque.
4. The vehicle following control method according to claim 1, wherein the determining of the tracking target point of the controlled vehicle according to the motion state sequence of the leading vehicle specifically comprises:
obtaining the yaw velocity, the course angle and the transverse displacement of the front vehicle according to the motion state sequence of the front vehicle;
and determining a tracking target point of the controlled vehicle according to the yaw velocity, the course angle and the transverse displacement of the front vehicle.
5. The vehicle following control method according to claim 1, wherein the determining an error model of the controlled vehicle according to the lateral dynamics equation of the controlled vehicle specifically comprises:
performing linear transformation on the lateral dynamic equation of the controlled vehicle to obtain a linearized form of the lateral dynamic equation of the controlled vehicle;
and subtracting the lateral dynamic equation of the controlled vehicle from the linearized form of the lateral dynamic equation of the controlled vehicle to determine an error model of the controlled vehicle.
6. The vehicle following control method according to claim 5, wherein the determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle specifically comprises:
adopting a positive Euler method to convert an error model of the controlled vehicle to obtain a system state equation of the controlled vehicle;
establishing an optimized objective function of a system state equation of the controlled vehicle according to the deviation of the minimum predicted output path point and the tracking target point;
and determining the steering wheel angle of the controlled vehicle according to the system state equation of the controlled vehicle by taking the optimization objective function as a constraint.
7. A vehicle following control system, characterized in that the system comprises:
the motion state sequence acquisition module of the front vehicle is used for acquiring the motion state sequence of the front vehicle;
the front vehicle and controlled vehicle error data determining module is used for determining the tracking distance error between the controlled vehicle and the front vehicle and the speed error between the controlled vehicle and the front vehicle according to the motion state sequence of the front vehicle;
the longitudinal target acceleration determining module is used for determining the longitudinal target acceleration of the controlled vehicle according to the tracking distance error between the controlled vehicle and the front vehicle, the speed error between the controlled vehicle and the front vehicle and the longitudinal acceleration of the controlled vehicle;
the tracking target point determining module is used for determining a tracking target point of the controlled vehicle according to the motion state sequence of the front vehicle;
the lateral dynamics equation determining module is used for establishing a lateral dynamics equation of the controlled vehicle according to the tracking target point;
the error model determination module of the controlled vehicle is used for determining an error model of the controlled vehicle according to a lateral dynamic equation of the controlled vehicle;
and the steering wheel angle determining module is used for determining the steering wheel angle of the controlled vehicle according to the error model of the controlled vehicle.
8. The vehicle following control system according to claim 7, wherein the motion state sequence obtaining module of the preceding vehicle specifically includes:
and the motion state sequence acquisition unit of the front vehicle is used for acquiring the motion state sequence of the front vehicle through V2X communication.
9. The vehicle following control system according to claim 7, further comprising:
the longitudinal target acceleration conversion module is used for converting the longitudinal target acceleration of the controlled vehicle into longitudinal required torque;
and the longitudinal control module is used for longitudinally controlling the controlled vehicle according to the longitudinal required torque.
10. The vehicle following control system according to claim 7, wherein the tracking target point determination module specifically includes:
the front vehicle motion data extraction unit is used for obtaining the yaw velocity, the course angle and the transverse displacement of the front vehicle according to the motion state sequence of the front vehicle;
and the tracking target point determining unit is used for determining the tracking target point of the controlled vehicle according to the yaw velocity, the course angle and the transverse displacement of the front vehicle.
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