CN111897213A - Automobile self-adaptive cruise control method under model uncertainty - Google Patents
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Abstract
The invention belongs to the technical field of vehicle control, and particularly relates to an automobile self-adaptive cruise control method under model uncertainty. A method for adaptive cruise control is provided, which models vehicle dynamics through adaptive online Bayesian inference, controls a current vehicle to realize constant-speed cruise based on a Lyapunov function, and adjusts the cruise speed of the vehicle in real time based on information of the distance between the vehicle and a preceding vehicle and a control barrier function to ensure safety. The invention can adaptively adjust the cruising speed according to the current road surface condition and the distance between the current road surface condition and the front vehicle, and is beneficial to ensuring the cruising safety.
Description
Technical Field
The invention belongs to the technical field of vehicle control, and particularly relates to an automobile self-adaptive cruise control method under model uncertainty.
Background
With the development of the times, the automatic constant-speed cruising system of the automobile plays an important role in the driving field. The cruise device can acquire the information of the front vehicle through a vehicle-mounted sensor, such as: distance, speed, etc. Then the driver can set the corresponding expected cruising speed, and the automatic cruising system can replace a person to efficiently execute tasks for a long time, so the automatic cruising system is widely applied to the field of unmanned driving. However, in a real environment, road gradient interference, a road friction coefficient change and the like may exist, a vehicle is difficult to obtain an accurate dynamic model, and in addition, a front vehicle may have rapid acceleration and deceleration behaviors. Therefore, for an automobile with limited motion space, in a task scene with extremely critical safety, an adaptive controller capable of adaptively adjusting the cruising speed of the automobile through workshop information to ensure safety needs to be designed.
In order to achieve the above object, the prior art autonomous cruise method needs an accurate vehicle dynamic model, as shown in patent 2017111478472, and provides a cruise control method, apparatus and system, which needs to obtain an accurate vehicle dynamic model during the modeling process, but when the road surface condition changes, such as the road surface has a steep slope and the road surface friction coefficient changes, the vehicle cannot stably reach the desired cruise speed, and at the same time, the safety of the vehicle may not be ensured, so that it is difficult to meet the cruise requirement of the driver.
Disclosure of Invention
The invention provides an automobile self-adaptive cruise control method under uncertain model for overcoming the defects in the prior art, and realizes the stable work and safe running of the automobile under uncertain dynamics environment.
In order to solve the technical problems, the invention adopts the technical scheme that: an automobile self-adaptive cruise control method under model uncertainty comprises the following steps:
s1, constructing a vehicle nonlinear affine system model, and then modeling uncertainty existing in a vehicle dynamics model based on a Gaussian process;
s2, acquiring distance information between vehicles and speed information of the vehicle based on a vehicle-mounted sensor;
s3, constructing a quadratic programming problem of inequality constraint by constructing a control Lyapunov function and combining with the uncertainty of an automobile model predicted by a Gaussian process based on the current speed of the automobile, and solving to obtain the optimal action uclfThe vehicle is controlled to quickly and gradually converge to the expected cruising speed, and the stability of the cruising speed is ensured under high confidence;
s4. based on expectationConstructing a control barrier function and combining model uncertainty of Gaussian process prediction to construct safety constraint by using the minimum workshop safety distance and the vehicle distance obtained by a vehicle-mounted sensor, and constraining the robot exploration and the action space in a safe feasible region under high confidence; constructing an inequality constrained quadratic programming problem, and correcting the optimal action u obtained in the step three under the minimum correctionclfModifying the operation into a security domain action set, and obtaining an optimal action u;
s5, applying the action u obtained by the vehicle to the interaction of the current vehicle and the environment;
s6, collecting the interaction state and action sequence of the vehicle and the environment on line, and updating a Gaussian process;
s7, repeating the steps S1 to S6 until the interaction is finished.
In the invention, the vehicle dynamics is modeled in real time through self-adaptive online Bayesian inference, and a safety optimization problem with constraints is constructed based on a control Lyapunov function and a control barrier function, so that the stable work and safe driving of the vehicle in an uncertain environment of dynamics are realized.
In one embodiment, in the step S1, the vehicle nonlinear affine system is modeled as:
wherein f(s) + g(s) a represents a prior model obtained by modeling system dynamics and kinematics, and d(s) represents the deviation of the model from the real environment.
In one embodiment, in the step S1, the vehicle dynamics model is in the specific form:
wherein p, v, a are the position, velocity and acceleration states of the vehicle, respectively; frIs aerodynamic drag; ffIs the rolling friction coefficient; m is the mass of the automobile; g is a gravitational acceleration constant; delta theta is the disturbance of unmodeled environmental road gradient and the like; wherein, Fr、FfAnd Δ θ isThere is a need for real-time modeled automotive dynamics uncertainty parameters.
In one embodiment, in the step S1, the data set is collected on-line
Wherein s istIs the state of the vehicle at time t, and n is the number of collected samples; gaussian process regression through Bayesian inference to obtain state s*Lower deviation d(s) of*) The mean values μ(s) and σ(s); obtaining a high confidence interval for d(s): d(s) { d | μ(s) -cσ(s)≤d≤μ(s)+cσ(s) }, where μ(s) is the mean of the deviations d(s), σ(s) is the standard deviation of the deviations d(s), c> 0 is a constant corresponding to the (1-) confidence in the Gaussian distribution.
In one embodiment, in the step S3, the concrete formula of the quadratic programming problem for controlling the lyapunov function and combining the uncertainty of the automobile model predicted by the gaussian process is:
s.t.LgV(s)u+LfV(s)+LμV(s)+cσ|LσV(s)|-κV(s)≤,
umin≤u≤umax,
where V(s) is a function of energy, u is vehicle motion, u ismax,uminThe diagonal matrix H(s) represents the weight of each dimension action, representing the slack variable and K corresponding to its upper and lower boundsIs a corresponding coefficient, cσThe notation L represents the Li derivative for the coefficient corresponding to the square term, and k is a function of class k.
In one embodiment, in the step S4, the specific formula of the constructed inequality-constrained quadratic programming problem is as follows:
s.t.-Lgh(s)u-Lfh(s)-Lμh(s)+cσ|Lσh(s)|-κh(s)≤η,
umin≤u≤umax,
where h(s) is a safety control barrier function, u is vehicle motion, u ismaxAnd uminCorresponding to its upper and lower bounds, the diagonal matrix P(s) represents the weight of each dimension of motion, η represents the relaxation variable and KηIs a corresponding coefficient, cσThe notation L represents the derivative of lie for the coefficient corresponding to the square term, and κ is a function of class k.
In one embodiment, the radial basis function kernel and the linear kernel are selected as Gaussian process kernel functions, and corresponding Gaussian process models are initialized.
In one embodiment, there are 4 vehicle initialization gaussian models, corresponding to the position and speed of the vehicle and the position and speed of the vehicle ahead.
In one embodiment, the state dimension of the current vehicle and the preceding vehicle is selected as the model input, the actual interaction error of the vehicle and the environment is selected as the output, and the data set(s) is collected on linet,at,st+1) And updating the Gaussian process model.
Compared with the prior art, the beneficial effects are: the invention provides an automobile self-adaptive cruise control method under model uncertainty, which solves the technical problems of unstable cruise speed and unsafe driving existing in the uncertainty of a self system and the uncertainty of an external environment in an automatic constant-speed cruise task with a nonlinear dynamics model vehicle limited in state and action space; the uncertainty of vehicle dynamics is modeled through a Gaussian process, a constrained quadratic programming problem is constructed based on control Lyapunov and control barrier functions, and a method for adaptively adjusting the cruising speed of a dynamically inaccurate vehicle based on inter-vehicle (V2V) information is achieved. The method realizes online self-adaptation to the environment under high confidence level by online collecting interaction data and Bayesian inference based on a Gaussian process so as to model uncertainty of interaction between a system dynamics model and the environment and ensure continuity of local Leptoschiz of the model; based on the control of the Lyapunov function and the Gaussian process, the stability of constant-speed cruising is ensured under high confidence; based on the control barrier function and the Gaussian process, the vehicle speed is adjusted, the vehicle action space is restricted in a safe feasible region under high confidence level, and the state in the unsafe region can be gradually converged into the safe region, so that the safety of the vehicle in the cruising process is ensured. .
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FIG. 1 is a schematic overall flow diagram of the process of the present invention.
FIG. 2 is a flow chart of the algorithm of the present invention.
Detailed Description
The drawings are for illustration purposes only and are not to be construed as limiting the invention; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
Example 1:
as shown in fig. 1, the invention provides an automobile adaptive cruise control method under model uncertainty, which comprises the following steps:
step 1: according to the method, a vehicle affine dynamics system model is constructed according to the priori knowledge of the vehicle dynamics and the actual task scene, and an expected cruising speed v is set. Selecting an expected cruising speed as a balance point according to a task scene, and designing a corresponding Control Lyapunov Function (CLF): v(s)t). Defining a safe state space of the vehicle according to the requirements: c ═ x | h (x) ≧ 0, such as: keeping a certain safe distance and a certain tracking distance with a front vehicle, and designing a corresponding Control Barrier Function (CBF): h(s)t)。
Step 2: in this example, a Radial Basis Function (RBF) kernel and a linear kernel are selected as gaussian process kernel functions, and corresponding gaussian process models are initialized. The number of the vehicle initialization gaussian models is 4, and the models respectively correspond to the position and the speed of the vehicle and the position and the speed of the front vehicle. The invention selects the current vehicle andthe state dimension of the vehicle in front is input as a model, the actual interaction error of the vehicle and the environment is output, and a data set(s) is collected on linet,at,st+1) And updating a Gaussian process model, as shown in FIG. 2, to realize modeling of vehicle dynamics and obtain Gaussian distribution of interaction uncertainty.
And step 3: obtaining the current speed of the vehicle based on a vehicle-mounted sensor, constructing stable constraint for the vehicle through a defined CLF function, solving the constraint through a Lagrange multiplier method, and obtaining a control quantity uclfThe speed of the vehicle is asymptotically converged to the set desired cruising speed.
And 4, step 4: constructing safety constraint for the vehicle through a defined CBF function based on the expected minimum inter-vehicle safety distance and the vehicle distance obtained by the vehicle-mounted sensor to correct the control quantity u in real timeclfUnder the minimum correction criterion, u is adjustedclfAnd modifying the state space of the vehicle into a safety domain action set to maintain the state space of the vehicle in a safety domain and obtain the optimal action u.
And 5: applying the action u obtained by the vehicle to the interaction of the current vehicle with the environment;
step 6: steps 2 to 6 are repeated until the desired target or maximum experimental set time is reached.
In the embodiment, in the automatic cruise system of the automobile, the controlled vehicle can adjust the expected cruise speed through the algorithm provided by the invention under the uncertainty caused by the road environment and the acceleration and deceleration of the front vehicle so as to ensure the safety, and the distance between the controlled vehicle and the front vehicle is kept within the safe distance.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. An automobile self-adaptive cruise control method under model uncertainty is characterized by comprising the following steps:
s1, constructing a vehicle nonlinear affine system model, and then modeling uncertainty existing in a vehicle dynamics model based on a Gaussian process;
s2, acquiring distance information between vehicles and speed information of the vehicle based on a vehicle-mounted sensor;
s3, constructing a quadratic programming problem of inequality constraint by constructing a control Lyapunov function and combining with the uncertainty of an automobile model predicted by a Gaussian process based on the current speed of the automobile, and solving to obtain the optimal action uclfThe vehicle is controlled to quickly and gradually converge to the expected cruising speed, and the stability of the cruising speed is ensured under high confidence;
s4, constructing a control barrier function based on the expected minimum workshop safety distance and the vehicle distance obtained by the vehicle-mounted sensor, combining model uncertainty of Gaussian process prediction to construct safety constraint, and constraining the robot exploration and the action space in a safe feasible region under high confidence; constructing an inequality constrained quadratic programming problem, and correcting the optimal action u obtained in the step three under the minimum correctionclfModifying the operation into a security domain action set, and obtaining an optimal action u;
s5, applying the action u obtained by the vehicle to the interaction of the current vehicle and the environment;
s6, collecting the interaction state and action sequence of the vehicle and the environment on line, and updating a Gaussian process;
s7, repeating the steps S1 to S6 until the interaction is finished.
2. The model-uncertain vehicle adaptive cruise control method according to claim 1, wherein in step S1, the vehicle nonlinear affine system is modeled as:
wherein f(s) + g(s) a represents the movement of the systemAnd (d) representing the deviation of the model from the real environment.
3. The method for controlling adaptive cruise for an automobile under model uncertainty according to claim 2, wherein in step S1, the vehicle dynamics model is embodied in the form of:
wherein p, v, a are the position, velocity and acceleration states of the vehicle, respectively; frIs aerodynamic drag; ffIs the rolling friction coefficient; m is the mass of the automobile; g is a gravitational acceleration constant; delta theta is the disturbance of unmodeled environmental road gradient and the like; wherein, Fr、FfAnd Δ θ are the vehicle dynamics uncertainty parameters that need to be modeled in real time.
4. The model-uncertain vehicle adaptive cruise control method according to claim 3, wherein in step S1, the data set is collected on-line
Wherein s istIs the state of the vehicle at time t, and n is the number of collected samples; gaussian process regression through Bayesian inference to obtain state s*Lower deviation d(s) of*) The mean values μ(s) and σ(s); a high confidence interval for d(s) is obtained.
5. The model-uncertain vehicle adaptive cruise control method according to claim 4, wherein the high confidence interval for d(s) is:
D(s)＝{d|μ(s)-cσ(s)≤d≤μ(s)+cσ(s) }; where μ(s) is the mean of the deviations d(s), σ(s) is the standard deviation of the deviations d(s), c> 0 is a constant corresponding to the (1-) confidence in the Gaussian distribution.
6. The model-uncertain automobile adaptive cruise control method according to claim 4, wherein in the step S3, a quadratic programming problem is constructed by combining the uncertainty of the automobile model predicted by the gaussian process and controlling the lyapunov function, and the specific formula of the inequality constraint quadratic programming problem is as follows:
s.t.LgV(s)u+LfV(s)+LμV(s)+cσ|LσV(s)|-κV(s)≤,
umin≤u≤umax,
where V(s) is a function of energy, u is vehicle motion, u ismax,uminThe diagonal matrix H(s) represents the weight of each dimension action, representing the slack variable and K corresponding to its upper and lower boundsIs a corresponding coefficient, cσThe notation L represents the Li derivative for the coefficient corresponding to the square term, and k is a function of class k.
7. The method for controlling adaptive cruise control for an automobile under model uncertainty according to claim 6, wherein in step S4, the specific formula of the quadratic programming problem with inequality constraints is:
s.t.-Lgh(s)u-Lfh(s)-Lμh(s)+cσ|Lσh(s)|-κh(s)≤η,
umin≤u≤umax,
where h(s) is a safety control barrier function, u is vehicle motion, u ismaxAnd uminCorresponding to its upper and lower bounds, the diagonal matrix P(s) represents the weight of each dimension of motion, η represents the relaxation variable and KηIs a corresponding coefficient, cσThe symbol L represents lie for the coefficient corresponding to the square termThe derivative of the equation, κ, is a function of the k class.
8. The model-uncertain automobile adaptive cruise control method according to claim 7, characterized in that a radial basis function kernel and a linear kernel are selected as gaussian process kernel functions, and a corresponding gaussian process model is initialized.
9. The model-uncertain vehicle adaptive cruise control method according to claim 8, wherein there are 4 vehicle-initialized gaussian models corresponding to the position and speed of the vehicle and the position and speed of the preceding vehicle, respectively.
10. Method for model-uncertain adaptive cruise control of a vehicle according to claim 9, characterized in that the state dimensions of the current vehicle and the vehicle in front are chosen as model inputs and the actual interaction error of the vehicle and the environment as output, by collecting the data set(s) on-line(s)t,at,st+1) And updating the Gaussian process model.
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| US11938929B2 (en) | 2021-12-15 | 2024-03-26 | Ford Global Technologies, Llc | Obstacle avoidance for vehicle with trailer |
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