CN113788021B - Adaptive following cruise control method combined with preceding vehicle speed prediction - Google Patents
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Abstract
The invention provides a self-adaptive following cruise control method combined with preceding vehicle speed prediction, which comprises the following steps of: step 10), constructing a front vehicle speed prediction model; step 20) obtaining the current operation condition information of the self vehicle and the front vehicle, and obtaining the speed of the front vehicle in the future time period by adopting the front vehicle speed prediction model; step 30) obtaining a safe vehicle distance by adopting a safe vehicle distance planning algorithm of a control invariant set according to the speed of the preceding vehicle in a future time period; and step 40) establishing a vehicle longitudinal dynamic system model, and calculating to obtain vehicle control parameters of the self vehicle by adopting a nonlinear model predictive control algorithm based on the safe vehicle distance obtained in the step 30). The invention combines the adaptive following cruise control method of the preceding vehicle speed prediction to predict the longitudinal speed of the preceding vehicle in the future time period, and constructs the time-varying safe following expected distance on the basis of the prediction, thereby realizing the safety-oriented and high-efficiency adaptive following.
Description
Technical Field
The invention belongs to the technical field of automatic driving of automobiles in intelligent traffic, and particularly relates to a self-adaptive following cruise control method combined with front automobile speed prediction.
Background
With the development of computer science, information science, control science and the like, the intellectualization and networking of the automobile are accelerated by the cross fusion between the disciplines. The progress in the field of automatic driving of automobiles also provides more possibilities for improving road traffic efficiency, road safety, energy conservation and emission reduction.
As a basic function in high-order auxiliary driving, the self-adaptive cruise control can take over an accelerator pedal to realize automatic control on longitudinal dynamics of the vehicle, and meanwhile, the distance and the speed of the front vehicle are sensed through a millimeter wave radar to realize automatic following of the front vehicle so as to prevent collision. Currently, adaptive cruise control has also made great progress with new sensors, communication systems and the addition of artificial intelligence. The automobile automatic driving system has the advantages that the automobile automatic driving system can sense the motion situation of not only the front vehicle in advance through farther front vehicle sensing distance, real-time communication information with lower time delay and a model with higher accuracy, and meanwhile the motion situation of the front traffic flow in a future period can be comprehensively predicted by combining road condition information of the front road. The method has important significance for improving various performance indexes of the automobile such as fuel economy, comfort, safety and the like.
The existing adaptive cruise control mainly adopts a constant-distance strategy and a constant-time-distance strategy as a vehicle-distance control strategy, and considers an adaptive method which is lack of nonlinear characteristics for acceleration and deceleration performance of vehicles under different road scenes and different weather environments. Meanwhile, the existing adaptive cruise control system depends on data output by sensors such as millimeter wave radar and ultrasonic radar, the resolution ratio of the identification of a target at a longer distance is still to be improved, and the traffic flow state under a dynamic traffic scene cannot be obtained.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method is characterized in that a front vehicle speed prediction-combined adaptive following cruise control method is provided, the longitudinal speed of a front vehicle in a future time period is predicted, a time-varying safe following expected distance is constructed on the basis of the prediction, and safe and efficient adaptive following is realized.
In order to solve the technical problem, the invention provides a self-adaptive following cruise control method combined with preceding vehicle speed prediction, which comprises the following steps:
step 10), constructing a front vehicle speed prediction model;
step 20) obtaining the current operation condition information of the self vehicle and the front vehicle, and obtaining the speed of the front vehicle in the future time period by adopting the front vehicle speed prediction model;
step 30) obtaining a safe vehicle distance by adopting a safe vehicle distance planning algorithm of a control invariant set according to the speed of the preceding vehicle in a future time period;
and step 40) establishing a vehicle longitudinal dynamic system model, and calculating to obtain vehicle control parameters of the self vehicle by adopting a nonlinear model predictive control algorithm based on the safe vehicle distance obtained in the step 30).
As a further improvement of the embodiment of the present invention, the step 10) specifically includes:
step 101) collecting vehicle driving condition data, wherein the vehicle driving condition data comprise self speed, acceleration, longitude, latitude and altitude of a vehicle in a typical road scene, and position and speed of a front vehicle relative to the self;
step 102) screening and cleaning the vehicle driving condition data, and dividing the vehicle driving condition data into a training data set and a verification data set;
step 103) constructing a long-short term memory neural network model with a memory door and forgetting door control function, wherein the long-short term memory neural network model has the input of the historical speed, the acceleration and the road gradient of the front vehicle and the output of the long-short term memory neural network model is the speed in the future time period;
and 104) inputting the training data set after normalization processing into the long and short term memory neural network model for training, and inputting the verification data set into the trained long and short term memory neural network model for cross verification to obtain a front vehicle speed prediction model.
As a further improvement of the embodiment of the present invention, the step 20) specifically includes:
step 201) obtaining current operation condition information of a preceding vehicle through a millimeter wave radar, wherein the current operation condition information of the preceding vehicle comprises the relative speed, the relative acceleration and the position relative to the own vehicle of the preceding vehicle;
step 202) obtaining current operation condition information of a self-vehicle through a satellite positioning system, wherein the current operation condition information of the self-vehicle comprises the position, the speed and the acceleration of the self-vehicle;
step 203) calculating to obtain the absolute position of the front vehicle according to the position of the front vehicle relative to the self vehicle and the position of the self vehicle; calculating to obtain the absolute speed of the front vehicle according to the relative speed of the front vehicle and the speed of the self vehicle; calculating to obtain the absolute acceleration of the front vehicle according to the relative acceleration of the front vehicle and the acceleration of the self vehicle;
step 204), acquiring the road gradient of the current position of the own vehicle and the highest and lowest speed limits of the current road through a geographic position information positioning system, the current position of the own vehicle and the current position of the preceding vehicle;
and step 205) inputting the absolute speed track, the absolute acceleration track and the gradient track of the preceding vehicle from the time when the preceding vehicle starts to follow to the current time into the preceding vehicle speed prediction model to obtain the speed track of the preceding vehicle in a future time period.
As a further improvement of the embodiment of the present invention, the step 30) specifically includes:
step 301) establishing a state space model of a front and rear vehicle following system shown in the vertical type (1):
wherein Δ s represents a distance between the subject vehicle and the preceding vehicle, v ego The speed of the vehicle is shown as the speed of the vehicle,
representing the predicted preceding vehicle speed, g (t) representing the longitudinal acceleration of the own vehicle,
representing the acceleration of the preceding vehicle;
step 302) adopts an allowance set shown in an equation (2) to represent state quantities in state space models of front and rear vehicle following systems:
wherein χ represents a permissible set of state variables,/ min Indicating the front vehicle and the self vehicle areMinimum safety distance required to be ensured in a delicate state;
step 303) defining a state constraint security set as a control invariant set, and expressing by adopting a formula (3):
where ψ represents a state constraint security set,
indicating the predicted minimum acceleration of the preceding vehicle, g (t) min Represents a minimum acceleration of the own vehicle; h represents the allowable set of the feasible accelerations of the front vehicle, and I represents the allowable set of the feasible accelerations of the self vehicle;
step 304) braking the current position of the front vehicle at the maximum deceleration, and calculating to obtain the braking distance and the final parking position of the front vehicle; backing the vehicle from the last parking position of the front vehicle by the minimum vehicle distance, recording the position of backing as the reverse recursion starting position of the vehicle, calculating the absolute value of the maximum deceleration of the vehicle from the stop starting position of the vehicle, accelerating the vehicle in the reverse direction until the speed of the vehicle is equal to the maximum speed of the vehicle allowed on the current road, and recording the acceleration distance of the vehicle in the process and the last position of the vehicle; and subtracting the maximum position of the vehicle accelerated to the maximum allowable speed of the road from the final position of the vehicle decelerated to be static, and obtaining the relative distance as the current safe vehicle distance.
As a further improvement of the embodiment of the present invention, the step 40) specifically includes:
step 401) establishing a vehicle longitudinal dynamics system model, which satisfies the formula (4):
wherein T represents a torque applied to the wheel, R represents a wheel radius, C D Denotes the wind resistance coefficient of the vehicle, A f Represents the frontal area of the vehicle, and rho represents the spaceThe air tightness, m represents the total assembly quality of the self-vehicle;
discretizing the vehicle longitudinal dynamics system model to satisfy equation (5):
x (t +1) ═ f (u (t), x (t), θ (t), t) formula (5)
Step 402), according to a vehicle longitudinal dynamic system model, considering a following distance error, comfort of drivers and passengers and energy consumption required by a vehicle, establishing a quadratic objective function of the vehicle longitudinal dynamic system shown in a formula (6):
in the formula, δ d (k | t) represents the desired vehicle distance cost soft constraint, Δ a (k | t) represents the acceleration of the own vehicle, and f (k | t → t) 0 ) Represents the cumulative energy consumption of the vehicle, R 1 A first weight coefficient, R, representing a cost function 2 A second weight coefficient, R, representing the cost function 3 A third weight coefficient representing the cost function;
step 403) establishing a constraint set of vehicle speed control according to the safe vehicle distance obtained in the step 30), wherein the constraint set comprises:
vehicle torque restraint: t is min ω(k|t)≤T(k|t)≤T max ω(k|t)，
And (3) vehicle speed extreme value constraint: v. of min ≤v(k|t)≤v max ，
And (4) safety distance constraint:
the expected vehicle distance soft constraint:
step 404) solving to obtain the vehicle control parameters of the vehicle.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects: the self-adaptive following cruise control method combined with the preceding vehicle speed prediction comprises the steps of firstly constructing a preceding vehicle speed prediction model, predicting the speed of a preceding vehicle in a future time period by using the preceding vehicle speed prediction model during driving, then obtaining a safe vehicle distance by using a safe vehicle distance planning algorithm of a control invariant set, and finally calculating by using a nonlinear model prediction control algorithm to obtain vehicle control parameters of the self-vehicle based on the safe vehicle distance. The method provided by the embodiment of the invention utilizes the sensor configuration and information acquisition mode of the existing vehicle, considers the condition that the front vehicle is manually driven, and realizes safe, efficient and energy-saving driving control of the vehicle under the condition of saving cost as much as possible.
Drawings
FIG. 1 is a flow chart of an adaptive following cruise control method according to an embodiment of the present invention;
FIG. 2 is a control system architecture diagram of an adaptive following cruise control method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a calculation method for obtaining a safe vehicle distance in the method according to the embodiment of the present invention.
FIG. 4 is a graph of simulation results of a method according to an embodiment of the present invention, wherein FIG. 4(a) is a vehicle speed graph, FIG. 4(b) is a vehicle distance graph, and FIG. 4(c) is a power consumption graph.
Detailed Description
The technical solution of the present invention will be explained in detail below.
The embodiment of the invention provides a self-adaptive following cruise control method combined with preceding vehicle speed prediction, which comprises the following steps as shown in figure 1:
step 10), constructing a front vehicle speed prediction model;
step 20) obtaining the current operation condition information of the self vehicle and the front vehicle, and obtaining the speed of the front vehicle in the future time period by adopting the front vehicle speed prediction model;
step 30) acquiring the road gradient of the preceding vehicle in the future time period, and obtaining the safe vehicle distance by adopting a safe vehicle distance planning algorithm of a control invariant set based on the speed of the preceding vehicle in the future time period and the road gradient of the preceding vehicle in the future time period;
and step 40) establishing a vehicle longitudinal dynamic system model, and calculating to obtain vehicle control parameters of the self vehicle by adopting a nonlinear model predictive control algorithm based on the safe vehicle distance obtained in the step 30).
The self-adaptive following cruise control method combined with the preceding vehicle speed prediction comprises the steps of firstly constructing a preceding vehicle speed prediction model, predicting the speed of a preceding vehicle in a future time period by using the preceding vehicle speed prediction model during driving, then obtaining a safe vehicle distance by using a safe vehicle distance planning algorithm of a control invariant set, and finally calculating by using a nonlinear model prediction control algorithm to obtain vehicle control parameters of the self-vehicle based on the safe vehicle distance. The method provided by the embodiment of the invention utilizes the sensor configuration and information acquisition mode of the existing vehicle, considers the condition that the front vehicle is manually driven, and realizes safe, efficient and energy-saving driving control of the vehicle under the condition of saving cost as much as possible.
Preferably, the step 10) specifically includes:
step 101), collecting vehicle driving condition data, wherein the vehicle driving condition data comprises self speed v (t), acceleration a (t), longitude lon (t), latitude lat (t) and altitude alt (t) of a self vehicle in a typical road scene, and position d of a front vehicle relative to the self vehicle r ( t ) And velocity v r (t) of (d). The number of data items collected should be 10 4 The above.
Step 102) screening and cleaning the vehicle driving condition data, and eliminating the data except the data in the scene of non-following vehicles. And segmenting the vehicle driving condition data in batches into a training data set and a verification data set. Wherein 200 pieces of data in the dataset are validated per segment. For example, the first 90% of the data is used as the training data set, and the remaining 10% is used as the verification data set.
And step 103) constructing a long-short term memory neural network model with a memory door and forgetting door control function, wherein the long-short term memory neural network model has the input of the historical speed, the acceleration and the road gradient of the front vehicle and the output of the long-short term memory neural network model is the speed in the future time period.
Step 104) in order to improve the accuracy of prediction, the training data set is normalized, specifically, a zero-mean normalization mode is adopted, that is, the average value of the data is obtained, and all the data are averaged by taking 0 as the mean value, and the specific formula is as follows:
in the formula, X i Representing a sequence of historical state vectors for the leading vehicle,
a sequence of average values representing historical state variables of the leading vehicle,
represents the average of the historical distance vectors of the preceding vehicles,
represents the average of the historical velocity vectors of the preceding vehicle,
represents the average of the historical acceleration vectors of the preceding vehicle,
an average value representing a gradient of a past driving road of the preceding vehicle, sig represents a standard deviation of each state variable of the preceding vehicle, X * Representing the processed historical state vector.
Inputting the normalized training set data into the long-short term memory neural network model for training, inputting the verification data set into the trained long-short term memory neural network model for cross verification, and adjusting the hyper-parameters of the neural network according to the training result until the prediction result of the neural network can meet the condition that the relative error value is less than a certain fixed value, thereby obtaining the front vehicle speed prediction model.
The root mean square error is introduced as a measurement standard, and the specific standard is that RMSE is less than or equal to 10 -2 . RMSE is defined as:
wherein Y (t) represents an actual observed value in the validation dataset,
represents the predicted estimate of the neural network output and N represents the dimension of the input variable to the neural network.
The preceding vehicle speed prediction model constructed by the embodiment of the invention can reasonably predict the speed of the preceding vehicle in a shorter time domain based on the strong learning capability of the long-short term memory network on the time sequence type data, thereby enhancing the driving safety, the energy saving performance and the high efficiency.
Preferably, the step 20) specifically includes:
step 201) obtaining the current operation condition information of the front vehicle through the millimeter wave radar. As shown in fig. 2, if the ID of the preceding vehicle is i, the time of starting to follow the preceding vehicle is i, and the current time is, the obtained current operating condition information of the preceding vehicle includes the relative speed v of the preceding vehicle r (t | i), relative acceleration a r (t | i) and a position d relative to the vehicle r (t|i)。
Step 202), obtaining current operation condition information of the self-vehicle through a satellite positioning system, wherein the current operation condition information of the self-vehicle comprises a position x (t), a speed v (t) and an acceleration a (t) of the self-vehicle.
Step 203) calculating the absolute position x of the preceding vehicle by using the formula (9) according to the position of the preceding vehicle relative to the own vehicle and the position of the own vehicle p (t) of (d). The absolute speed v of the preceding vehicle is calculated by the formula (10) based on the relative speed of the preceding vehicle and the speed of the own vehicle p (t) of (d). The absolute acceleration a of the preceding vehicle is calculated by the equation (11) based on the relative acceleration of the preceding vehicle and the acceleration of the own vehicle p (t)。
x p (t)＝x(t)+d r (t | i) formula (9)
v p (t)＝v(t)+v r (t | i) formula (10)
a p (t)＝a(t)+a r (t | i) formula (11)
Step 203) positioning the system through the geographic position information, and the position x (t) of the self vehicle and the absolute position x of the front vehicle p (t) obtaining road slopes θ (t) and θ of the current position of the host vehicle p (t), the highest speed limit v of the road where the vehicle is currently located min With the lowest limit speed v max 。
Step 204) inputting the absolute speed track, the absolute acceleration track and the gradient track of the preceding vehicle from the time when the preceding vehicle starts to follow to the current time into the speed prediction model of the preceding vehicle to obtain the speed track of the preceding vehicle in the future time period
Preferably, as shown in fig. 3, step 30) specifically includes:
step 301) establishing a state space model of a front and rear vehicle following system shown in the vertical type (1):
wherein Δ s represents a distance between the host vehicle and the preceding vehicle, v ego The speed of the vehicle is shown as the speed of the vehicle,
representing the predicted preceding vehicle speed, g (t) representing the longitudinal acceleration of the own vehicle,
indicating the acceleration of the preceding vehicle.
Step 302) adopts an allowance set shown in an equation (2) to represent state quantities in state space models of front and rear vehicle following systems:
wherein χ represents a permissible set of state variables,/ min Indicating the position of the front vehicle and the self vehicleSo that a minimum safety distance needs to be guaranteed in the situation.
Step 303) defining a state constraint security set as a control invariant set, and expressing by adopting a formula (3):
where ψ represents a state constraint security set,
indicating the predicted minimum acceleration of the preceding vehicle, g (t) min Represents a minimum acceleration of the own vehicle; h represents the allowable set of possible accelerations of the preceding vehicle, and I represents the allowable set of possible accelerations of the own vehicle.
Step 304) from the previous vehicle the current position x p (t) at maximum deceleration a pmin Braking, calculating the braking distance and the last parking position x of the front vehicle pn 。
Retreating from the last parking position of the preceding vehicle by a minimum vehicle distance l min Recording the backward position as the reverse recursion starting position of the vehicle, and calculating the absolute value of the maximum deceleration of the vehicle from the reverse recursion starting position, accelerating the vehicle in the reverse direction from the rest state until the speed of the vehicle is equal to the maximum speed v allowed by the vehicle on the current road max Recording the acceleration distance x of the bicycle during the period acl And the last position x of the own vehicle n 。
Accelerating the vehicle to the maximum position x of the maximum allowable speed of the road n And the final position x of the preceding vehicle decelerating to a standstill pn Subtracting the distance to obtain the current safe distance d safe 。
The method of the embodiment of the invention adopts the safe vehicle distance obtained by the control invariant set to better adapt to different traffic conditions, and dynamically considers the dynamic characteristics of the front vehicle in the prediction time domain. Compared with the traditional invariant set algorithm, the method is relatively non-conservative on the premise of ensuring safety, can achieve higher car following efficiency and traffic flow density, and further improves economy and comfort.
Preferably, the step 40) specifically includes:
step 401) establishing a vehicle longitudinal dynamics system model, which satisfies the formula (4):
wherein T represents a torque applied to the wheel, R represents a wheel radius, C D Denotes the wind resistance coefficient of the vehicle, A f The area of the windward side of the vehicle is shown, rho represents the air density, m represents the total loading mass of the vehicle, and theta represents the road gradient.
Discretizing the vehicle longitudinal dynamics system model to satisfy equation (5):
x (t +1) ═ f (u (t), x (t), θ (t), t) formula (5)
Wherein, the control variable of the system is the torque T (t) of the wheels of the vehicle, and the state variable is the speed v (t) and the accumulated energy consumption f (t | t) of the vehicle 0 )。
Step 402), according to a vehicle longitudinal dynamic system model, considering a following distance error, comfort of drivers and passengers and energy consumption required by a vehicle, establishing a quadratic objective function of the vehicle longitudinal dynamic system shown in a formula (6):
in the formula, δ d (k | t) represents a desired vehicle distance cost soft constraint, here representing a vehicle; Δ a (k | t) represents the acceleration of the vehicle, and represents the comfort of the vehicle; f (k | t → t) 0 ) The energy-saving performance of the vehicle is represented by the accumulated energy consumption of the vehicle; r 1 A first weight coefficient, R, representing a cost function 2 A second weight coefficient, R, representing the cost function 3 A third weight coefficient representing the cost function.
Step 403) establishing a constraint set C for controlling the vehicle speed according to the safe vehicle distance obtained in the step 30), wherein the constraint set comprises:
vehicle torque restraint: t is a unit of min ω(k|t)≤T(k|t)≤T max ω(k|t)，
And (3) vehicle speed extreme value constraint: v. of min ≤v(k|t)≤v max ，
And (4) safety distance constraint:
the expected vehicle distance soft constraint:
And step 404), solving the established model predictive control problem by taking 20s as the predicted time domain length and 0.5s as the sampling time step length.
And finally, solving the nonlinear speed planning problem to obtain the wheel torque control quantity, namely the required control output. The torque of the vehicle is sent to a lower controller, and the lower controller is responsible for converting the signal and then sending the signal to an actuating mechanism for execution.
The invention adopts a nonlinear model predictive control algorithm, can display and express the state quantity and the control quantity constraint of the system, and conveniently constructs an objective function to realize different control target requirements.
The method of the embodiment of the invention is simulated, and the simulation result is shown in FIG. 4. The preceding vehicle speed prediction model in the embodiment of the invention shown in fig. 4(a) can reasonably predict the actual vehicle speed of the preceding vehicle, and the curve cluster of the predicted vehicle speed can better fit the actual preceding vehicle speed curve. Under different control strategies, the vehicle speed curve of the safe vehicle distance planning algorithm provided by the invention is smoother than the vehicle speed curve adopting the self-adaptive cruise control algorithm, so that a plurality of unnecessary acceleration and deceleration behaviors are reduced, and the comfort of passengers is improved. It can be seen from fig. 4(b) that the vehicle distance change by the method of the embodiment of the present invention is smoother, and the vehicle distance by the normal adaptive cruise algorithm has a certain jitter at the beginning, which causes unnecessary energy waste and uncomfortable feeling. In fig. 4(c), a pure electric vehicle is taken as an example, the method in the embodiment of the present invention can better save the electric energy consumption of the vehicle, and simultaneously, the torque output of the vehicle is smoother compared with the conventional adaptive cruise, and can better meet the requirements of comfort and energy saving.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to further illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the claims and their equivalents.
Claims (4)
1. An adaptive following cruise control method combined with a preceding vehicle speed prediction is characterized by comprising the following steps:
step 10), constructing a front vehicle speed prediction model;
step 20) obtaining the current operation condition information of the self vehicle and the front vehicle, and obtaining the speed of the front vehicle in the future time period by adopting the front vehicle speed prediction model;
step 30) obtaining a safe vehicle distance by adopting a safe vehicle distance planning algorithm of a control invariant set according to the speed of the preceding vehicle in a future time period;
step 40) establishing a vehicle longitudinal dynamics system model, and calculating to obtain vehicle control parameters of the self vehicle by adopting a nonlinear model predictive control algorithm based on the safe vehicle distance obtained in the step 30);
the step 10) specifically comprises:
step 101) collecting vehicle driving condition data, wherein the vehicle driving condition data comprise self speed, acceleration, longitude, latitude and altitude of a vehicle in a typical road scene, and position and speed of a front vehicle relative to the self;
step 102) screening and cleaning the vehicle driving condition data, and dividing the vehicle driving condition data into a training data set and a verification data set;
step 103) constructing a long-short term memory neural network model with a memory door and forgetting door control function, wherein the long-short term memory neural network model has the input of the historical speed, the acceleration and the road gradient of the front vehicle and the output of the long-short term memory neural network model is the speed in the future time period;
and 104) inputting the training data set after normalization processing into the long and short term memory neural network model for training, and inputting the verification data set into the trained long and short term memory neural network model for cross verification to obtain a front vehicle speed prediction model.
2. The adaptive cruise control with following vehicle combined with preceding vehicle speed prediction according to claim 1, characterized in that said step 20) comprises in particular:
step 201) obtaining current operation condition information of a preceding vehicle through a millimeter wave radar, wherein the current operation condition information of the preceding vehicle comprises the relative speed, the relative acceleration and the position relative to the own vehicle of the preceding vehicle;
step 202) obtaining current operation condition information of a self-vehicle through a satellite positioning system, wherein the current operation condition information of the self-vehicle comprises the position, the speed and the acceleration of the self-vehicle;
step 203) calculating to obtain the absolute position of the front vehicle according to the position of the front vehicle relative to the self vehicle and the position of the self vehicle; calculating to obtain the absolute speed of the front vehicle according to the relative speed of the front vehicle and the speed of the self vehicle; calculating to obtain the absolute acceleration of the front vehicle according to the relative acceleration of the front vehicle and the acceleration of the self vehicle;
step 204), acquiring the road gradient of the current position of the own vehicle and the highest and lowest speed limits of the current road through a geographic position information positioning system, the current position of the own vehicle and the current position of the previous vehicle;
step 205) inputting the absolute speed track, the absolute acceleration track and the gradient track of the preceding vehicle from the time when the preceding vehicle starts to follow to the current time into the speed prediction model of the preceding vehicle to obtain the speed track of the preceding vehicle in the future time period.
3. The adaptive cruise control with following vehicle combined with preceding vehicle speed prediction according to claim 1, characterized in that said step 30) comprises in particular:
step 301) establishing a state space model of a front and rear vehicle following system shown in the vertical type (1):
wherein Δ s represents a distance between the subject vehicle and the preceding vehicle, v ego The speed of the vehicle is shown as the speed of the vehicle,
representing the predicted preceding vehicle speed, g (t) representing the longitudinal acceleration of the own vehicle,
representing the acceleration of the preceding vehicle;
step 302) adopts an allowance set shown in an equation (2) to represent state quantities in state space models of front and rear vehicle following systems:
wherein χ represents a permissible set of state variables,/ min The minimum safety distance required to be ensured between the front vehicle and the self vehicle in an exquisite state is represented;
step 303) defining a state constraint security set as a control invariant set, and expressing by adopting a formula (3):
where ψ represents a state constraint security set,
indicating the predicted minimum acceleration of the preceding vehicle, g (t) min Represents a minimum acceleration of the own vehicle; h represents the allowable set of the feasible acceleration of the front vehicle, and I represents the allowable set of the feasible acceleration of the self vehicle;
step 304) braking the current position of the front vehicle at the maximum deceleration, and calculating to obtain the braking distance and the final parking position of the front vehicle; backing the vehicle from the last parking position of the front vehicle by the minimum vehicle distance, recording the position of backing as the reverse recursion starting position of the vehicle, calculating the absolute value of the maximum deceleration of the vehicle from the stop starting position of the vehicle, accelerating the vehicle in the reverse direction until the speed of the vehicle is equal to the maximum speed of the vehicle allowed on the current road, and recording the acceleration distance of the vehicle in the process and the last position of the vehicle; and subtracting the maximum position of the vehicle accelerated to the maximum allowable speed of the road from the final position of the vehicle decelerated to be static, and obtaining the relative distance as the current safe vehicle distance.
4. The adaptive cruise control with following vehicle combined with preceding vehicle speed prediction according to claim 1, characterized in that said step 40) comprises in particular:
step 401) establishing a vehicle longitudinal dynamics system model, which satisfies the formula (4):
wherein T represents a revolution applied to the wheelMoment, R represents wheel radius, C D Denotes the wind resistance coefficient of the vehicle, A f The area of the windward side of the vehicle is represented, rho represents the air density, and m represents the total assembly mass of the vehicle;
discretizing the vehicle longitudinal dynamics system model to satisfy equation (5):
x (t +1) ═ f (u (t), x (t), θ (t), t) formula (5)
Step 402), according to a vehicle longitudinal dynamic system model, considering a following distance error, comfort of drivers and passengers and energy consumption required by a vehicle, establishing a quadratic objective function of the vehicle longitudinal dynamic system shown in a formula (6):
in the formula, δ d (k | t) represents the desired vehicle distance cost soft constraint, Δ a (k | t) represents the acceleration of the own vehicle, and f (k | t → t) 0 ) Represents the cumulative energy consumption of the vehicle, R 1 A first weight coefficient, R, representing a cost function 2 A second weight coefficient, R, representing the cost function 3 A third weight coefficient representing a cost function;
step 403) establishing a constraint set of vehicle speed control according to the safe vehicle distance obtained in the step 30), wherein the constraint set comprises:
vehicle torque restraint: t is min ω(k|t)≤T(k|t)≤T max ω(k|t)，
And (3) vehicle speed extreme value constraint: v. of min ≤v(k|t)≤v max ，
And (4) safety distance constraint:
the expected vehicle distance soft constraint:
wherein the expected distance is added to the objective function shown in the formula (6) in the form of soft constraint;
step 404) solving to obtain the vehicle control parameters of the vehicle.
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| CN114913714A (en) * | 2022-05-05 | 2022-08-16 | 中国第一汽车股份有限公司 | Method for determining a safe distance of a vehicle |
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