CN111897213A - A car adaptive cruise control method under model uncertainty - Google Patents
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Abstract
本发明属于车辆控制技术领域,更具体地,涉及一种模型不确定下的汽车自适应巡航控制方法。提供用于自适应巡航控制的方法,所述方法通过自适应在线贝叶斯推理对车辆动力学进行建模,基于李雅普诺夫函数以控制当前车辆实现定速巡航,并基于车辆和前车的距离的信息和控制屏障函数来实时调节车辆的巡航速度以保证安全性。本发明能够根据当前的路面状况以及和前车的距离来自适应调整巡航速度,有利于保证巡航的安全性。
The invention belongs to the technical field of vehicle control, and more particularly, relates to a method for vehicle adaptive cruise control under model uncertainty. Provides a method for adaptive cruise control that models vehicle dynamics through adaptive online Bayesian inference, based on a Lyapunov function to control the current vehicle for cruise control, and based on the The distance information and control barrier function are used to adjust the cruising speed of the vehicle in real time to ensure safety. The invention can adaptively adjust the cruising speed according to the current road conditions and the distance from the preceding vehicle, which is beneficial to ensure the safety of cruising.
Description
技术领域technical field
本发明属于车辆控制技术领域,更具体地,涉及一种模型不确定下的汽车自适应巡航控制方法。The invention belongs to the technical field of vehicle control, and more particularly, relates to a method for vehicle adaptive cruise control under model uncertainty.
背景技术Background technique
随着时代的发展,汽车自动定速巡航系统在驾驶领域中发挥着重要作用。巡航设备可以通过车载传感器获取前车信息,如:距离、速度等。而后驾驶员可设置相应的期望巡航速度,自动巡航系统能够代替人长时间高效地执行任务,因此在无人驾驶领域得到广泛应用。但现实的环境可能存在路面梯度干扰,路面摩擦系数改变等情况,车辆难以获取精确的动力学模型,此外前方车辆可能存在急剧加速和减速行为。因此对于动作空间受限的汽车,在安全性极其关键的任务场景中,需要设计一个能通过车间信息来自适应调节车辆巡航速度以保证安全性的自适应控制器。With the development of the times, the automatic cruise control system of automobiles plays an important role in the field of driving. The cruise device can obtain the information of the preceding vehicle, such as distance, speed, etc., through the on-board sensors. Then the driver can set the corresponding desired cruise speed, and the automatic cruise system can replace the human to perform tasks efficiently for a long time, so it is widely used in the field of unmanned driving. However, in the real environment, there may be road gradient interference and changes in the friction coefficient of the road surface. It is difficult for the vehicle to obtain an accurate dynamic model. In addition, the vehicle ahead may have rapid acceleration and deceleration behaviors. Therefore, for vehicles with limited action space, in mission scenarios where safety is extremely critical, it is necessary to design an adaptive controller that can adaptively adjust the cruising speed of the vehicle through the workshop information to ensure safety.
为达到上述的目标,现有技术中自主巡航的方法需要精确的车辆动力学模型,如专利2017111478472,提供的一种巡航控制方法、装置及系统,在建模过程中需要获取精确的车辆动力学模型,但是,在路面状况发生变化时,如路面存在起伏陡坡、路面摩擦系数改变时,车辆无法稳定到达期望的巡航速度,同时可能无法保证车辆的安全性,从而难以满足驾驶员的巡航需求。In order to achieve the above goals, the method of autonomous cruise in the prior art requires an accurate vehicle dynamics model. For example, the patent 2017111478472 provides a cruise control method, device and system. During the modeling process, it is necessary to obtain accurate vehicle dynamics. However, when the road conditions change, such as when the road surface has undulating steep slopes and the road friction coefficient changes, the vehicle cannot stably reach the desired cruising speed, and at the same time, the safety of the vehicle may not be guaranteed, making it difficult to meet the driver's cruising needs.
发明内容SUMMARY OF THE INVENTION
本发明为克服上述现有技术中的缺陷,提供一种模型不确定下的汽车自适应巡航控制方法,实现了车辆在动力学不确定环境下的稳定性工作和安全行驶。In order to overcome the above-mentioned defects in the prior art, the present invention provides a vehicle adaptive cruise control method under model uncertainty, which realizes the stable operation and safe driving of the vehicle under the dynamic uncertainty environment.
为解决上述技术问题,本发明采用的技术方案是:一种模型不确定下的汽车自适应巡航控制方法,包括以下步骤:In order to solve the above-mentioned technical problem, the technical solution adopted in the present invention is: a method for auto adaptive cruise control under model uncertainty, comprising the following steps:
S1.构建车辆非线性仿射系统模型,然后基于高斯过程,对车辆动力学模型存在的不确定性进行建模;S1. Build a vehicle nonlinear affine system model, and then model the uncertainty existing in the vehicle dynamics model based on a Gaussian process;
S2.基于车载传感器获取车辆间距离信息,以及本车的速度信息;S2. Obtain the distance information between vehicles and the speed information of the vehicle based on the on-board sensors;
S3.基于本车的当前速度,构建控制李雅普诺夫函数并结合高斯过程预测的汽车模型不确定性,构建不等式约束的二次规划问题,求解得到最佳动作uclf以控制车辆,让其速度快速渐进收敛到期望巡航速度,在高置信度下保证巡航速度的稳定性;S3. Based on the current speed of the vehicle, construct a control Lyapunov function combined with the uncertainty of the vehicle model predicted by the Gaussian process, construct a quadratic programming problem with inequality constraints, and solve to obtain the optimal action u clf to control the vehicle and let its speed Rapid and gradual convergence to the desired cruise speed, ensuring the stability of the cruise speed under high confidence;
S4.基于期望的最小车间安全距离和车载传感器获得的车辆间距,构建控制屏障函数并结合高斯过程预测的模型不确定性,以构造安全约束,在高置信度下将机器人探索和动作空间约束在安全可行域内;构建不等式约束的二次规划问题,在最小校正下将步骤三得到的最佳动作uclf修正至安全域动作集合内,并得到最佳动作u*;S4. Based on the expected minimum safety distance between vehicles and the distance between vehicles obtained by on-board sensors, construct a control barrier function combined with the model uncertainty predicted by the Gaussian process to construct a safety constraint that constrains the robot exploration and action space at high confidence In the safe feasible domain; construct a quadratic programming problem with inequality constraints, correct the best action u clf obtained in step 3 into the action set of the safe domain under the minimum correction, and obtain the best action u*;
S5.将车辆获得的动作u*应用于当前车辆的与环境的交互中;S5. Apply the action u* obtained by the vehicle to the interaction of the current vehicle with the environment;
S6.在线收集车辆与环境交互的状态和动作序列,更新高斯过程;S6. Collect the state and action sequence of the interaction between the vehicle and the environment online, and update the Gaussian process;
S7.重复步骤S1至步骤S6直至交互结束。S7. Repeat steps S1 to S6 until the interaction ends.
在本发明中,通过自适应在线贝叶斯推理对车辆动力学进行实时建模,基于控制李雅普诺夫函数和控制屏障函数构造带约束安全优化问题,实现车辆在动力学不确定环境下的稳定性工作和安全行驶。In the present invention, real-time modeling of vehicle dynamics is performed through adaptive online Bayesian inference, and a constrained safety optimization problem is constructed based on the control Lyapunov function and the control barrier function, so as to realize the stability of the vehicle under the dynamic uncertainty environment. Sex work and safe driving.
在其中一个实施例中,所述的S1步骤中,车辆非线性仿射系统建模为:其中f(s)+g(s)a代表对系统动力学、运动学建模所得的先验模型,d(s)代表模型与真实环境的偏差。In one of the embodiments, in the step S1, the vehicle nonlinear affine system is modeled as: where f(s)+g(s)a represents the prior model obtained by modeling system dynamics and kinematics, and d(s) represents the deviation of the model from the real environment.
在其中一个实施例中,所述的S1步骤中,车辆动力学模型的具体形式为:In one embodiment, in the step S1, the specific form of the vehicle dynamics model is:
式中,p、v、a分别是汽车的位置、速度和加速度状态;Fr是空气动力学阻力;Ff是滚动摩擦系数;M是汽车的质量;g是重力加速度常数;Δθ是未建模的环境路面梯度等干扰;其中,Fr、Ff和Δθ是需要实时建模的汽车动力学不确定性参数。In the formula, p, v, a are the position, speed and acceleration state of the car, respectively; F r is the aerodynamic resistance; F f is the rolling friction coefficient; M is the mass of the car; g is the gravitational acceleration constant; Among them, F r , F f and Δθ are the uncertain parameters of vehicle dynamics that need to be modeled in real time.
在其中一个实施例中,在所述的S1步骤中,通过在线收集的数据集其中st是t时刻车辆的状态,n为采集样本的数目;高斯过程回归通过贝叶斯推理得到状态s*的下偏差d(s*)的均值μ(s)和σ(s);得到关于d(s)的高置信区间:D(s)={d|μ(s)-cδσ(s)≤d≤μ(s)+cδσ(s)},其中μ(s)是偏差d(s)的均值,σ(s)是偏差d(s)的标准差,cδ>0是高斯分布中(1-δ)置信度对应的常数。In one of the embodiments, in the step S1, through the data set collected online where s t is the state of the vehicle at time t, and n is the number of samples collected; Gaussian process regression obtains the mean μ(s) and σ(s) of the lower deviation d(s * ) of the state s * through Bayesian inference; High confidence interval for d(s): D(s)={d|μ(s)-c δ σ(s)≤d≤μ(s)+c δ σ(s)}, where μ(s) is the mean of the deviation d(s), σ(s) is the standard deviation of the deviation d(s), and c δ > 0 is the constant corresponding to the (1-δ) confidence in the Gaussian distribution.
在其中一个实施例中,在所述的S3步骤中,构建控制李雅普诺夫函数并结合高斯过程预测的汽车模型不确定性,构建不等式约束的二次规划问题的具体公式为:In one embodiment, in the step S3, the control Lyapunov function is constructed and combined with the uncertainty of the vehicle model predicted by the Gaussian process, and the specific formula for constructing an inequality-constrained quadratic programming problem is:
s.t.LgV(s)u+LfV(s)+LμV(s)+cσ|LσV(s)|-κV(s)≤ε,stL g V(s)u+L f V(s)+L μ V(s)+c σ |L σ V(s)|-κV(s)≤ε,
umin≤u≤umax,u min ≤u≤u max ,
式中,V(s)为能量函数,u为车辆动作,umax,umin则对应其上界与下界,对角矩阵H(s)代表每一维动作的权重,ε代表松弛变量而Kε是对应的系数,cσ为方项对应的系数,符号L代表李氏导数,κ为一个k类函数。In the formula, V(s) is the energy function, u is the vehicle action, u max , u min correspond to its upper and lower bounds, the diagonal matrix H(s) represents the weight of each dimension, ε represents the slack variable and K ε is the corresponding coefficient, c σ is the coefficient corresponding to the square term, the symbol L represents the Li's derivative, and κ is a k-type function.
在其中一个实施例中,所述的S4步骤中,构建的不等式约束的二次规划问题的具体公式为:In one embodiment, in the step S4, the specific formula of the constructed inequality-constrained quadratic programming problem is:
s.t.-Lgh(s)u-Lfh(s)-Lμh(s)+cσ|Lσh(s)|-κh(s)≤η,st-L g h(s)uL f h(s)-L μ h(s)+c σ |L σ h(s)|-κh(s)≤η,
umin≤u≤umax,u min ≤u≤u max ,
式中,h(s)为安全控制屏障函数,u是车辆动作,umax和umin则对应其上界与下界,对角矩阵P(s)代表每一维动作的权重,η代表松弛变量而Kη是对应的系数,cσ为方项对应的系数,符号L代表李氏导数,κ是一个k类函数。In the formula, h(s) is the safety control barrier function, u is the vehicle action, u max and u min correspond to its upper and lower bounds, the diagonal matrix P(s) represents the weight of each dimension, and η represents the slack variable And K η is the corresponding coefficient, c σ is the coefficient corresponding to the square term, the symbol L stands for Li's derivative, and κ is a k-type function.
在其中一个实施例中,选取径向基函数核和线性核为高斯过程核函数,初始化相应高斯过程模型。In one embodiment, the radial basis function kernel and the linear kernel are selected as Gaussian process kernel functions, and the corresponding Gaussian process model is initialized.
在其中一个实施例中,车辆初始化高斯模型有4个,分别对应车辆的位置、速度以及前车的位置、速度。In one of the embodiments, there are four initialized Gaussian models for the vehicle, respectively corresponding to the position and speed of the vehicle and the position and speed of the preceding vehicle.
在其中一个实施例中,选取当前车辆和前方车辆的状态维度为模型输入,车辆和环境的实际交互误差为输出,通过在线收集数据集(st,at,st+1),更新高斯过程模型。In one embodiment, the state dimension of the current vehicle and the preceding vehicle is selected as the model input, the actual interaction error between the vehicle and the environment is the output, and the Gaussian is updated by collecting the data set (s t , at , s t +1 ) online. process model.
与现有技术相比,有益效果是:本发明提供的一种模型不确定下的汽车自适应巡航控制方法,解决了非线性动力学模型车辆在状态、动作空间受限的自动定速巡航任务中,面对自身系统不确定性和外部环境不确定性存在巡航速度不稳定、行驶不安全的技术问题;通过高斯过程对车辆动力学的不确定性进行建模,基于控制李雅普诺夫和控制屏障函数构造带约束二次规划问题,实现基于车辆间(V2V)信息来适应性地调节动力学不精确的车辆巡航的速度的方法。本发明基于高斯过程通过在线收集交互数据及贝叶斯推理在高置信度下实现对环境的在线自适应,以建模系统动力学模型和环境交互的不确定性,并保证模型局部李普希兹连续;基于控制李雅普诺夫函数和高斯过程,在高置信度下保证定速巡航的稳定性;基于控制屏障函数和高斯过程,调节车辆速度在高置信度下将车辆动作空间约束在安全可行域内,且可将不安全域内的状态渐进收敛到安全域内,保证车辆在巡航过程中的安全性。。Compared with the prior art, the beneficial effects are as follows: the invention provides a vehicle adaptive cruise control method under model uncertainty, which solves the task of automatic cruise control at a limited state and action space of a nonlinear dynamic model vehicle. In the system, there are technical problems of unstable cruising speed and unsafe driving in the face of the uncertainty of its own system and the uncertainty of the external environment; the uncertainty of vehicle dynamics is modeled by Gaussian process, based on control Lyapunov and control The barrier function constructs a constrained quadratic programming problem and implements a method for adaptively adjusting the speed of dynamically imprecise vehicles cruising based on vehicle-to-vehicle (V2V) information. Based on the Gaussian process, the present invention realizes online self-adaptation to the environment through online collection of interactive data and Bayesian inference under high confidence, so as to model the uncertainty of the system dynamics model and the environment interaction, and to ensure the local Lipschitz continuity of the model. ; Based on the control Lyapunov function and Gaussian process, the stability of cruise control is guaranteed under high confidence; based on the control barrier function and Gaussian process, the vehicle speed is adjusted to constrain the vehicle action space in the safe and feasible region under high confidence, And the state in the unsafe domain can be gradually converged into the safe domain to ensure the safety of the vehicle during the cruise. .
附图说明Description of drawings
图1是本发明方法的整体流程示意图。FIG. 1 is a schematic diagram of the overall flow of the method of the present invention.
图2是本发明算法的流程示意图。FIG. 2 is a schematic flowchart of the algorithm of the present invention.
具体实施方式Detailed ways
附图仅用于示例性说明,不能理解为对本发明的限制;为了更好说明本实施例,附图某些部件会有省略、放大或缩小,并不代表实际产品的尺寸;对于本领域技术人员来说,附图中某些公知结构及其说明可能省略是可以理解的。附图中描述位置关系仅用于示例性说明,不能理解为对本发明的限制。The accompanying drawings are for illustrative purposes only, and should not be construed as limiting the present invention; in order to better illustrate the present embodiment, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art It is understandable to the artisan that certain well-known structures and descriptions thereof may be omitted from the drawings. The positional relationships described in the drawings are only for exemplary illustration, and should not be construed as limiting the present invention.
实施例1:Example 1:
如图1所示,本发明提供一种模型不确定下的汽车自适应巡航控制方法,包括以下步骤:As shown in FIG. 1 , the present invention provides a method for vehicle adaptive cruise control under model uncertainty, including the following steps:
步骤1:本实例根据汽车动力学的先验知识和实际的任务场景,构建车辆仿射动力学系统模型,并设置一期望巡航速度v。根据任务场景选取期望巡航速度为平衡点,设计相应控制李雅普诺夫函数(CLF):V(st)。根据需求定义车辆的安全状态空间:C={x|h(x)≥0},如:与前车保持一定的安全距离和跟踪距离,设计相应的控制屏障函数(CBF):h(st)。Step 1: This example builds a vehicle affine dynamics system model based on the prior knowledge of vehicle dynamics and actual task scenarios, and sets a desired cruise speed v. According to the task scenario, the expected cruise speed is selected as the balance point, and the corresponding control Lyapunov function (CLF) is designed: V(s t ). Define the safe state space of the vehicle according to the requirements: C={x|h(x)≥0}, such as: keep a certain safety distance and tracking distance from the preceding vehicle, design the corresponding control barrier function (CBF): h(s t ).
步骤2:本实例中,选取径向基函数(RBF)核和线性核为高斯过程核函数,初始化相应高斯过程模型。车辆初始化高斯模型有4个,分别对应车辆的位置、速度以及前车的位置、速度。本发明选取当前车辆和前方车辆的状态维度为模型输入,车辆和环境的实际交互误差为输出,通过在线收集数据集(st,at,st+1),更新高斯过程模型,如图2所示,实现对车辆动力学进行建模,得出交互不确定性的高斯分布。Step 2: In this example, the radial basis function (RBF) kernel and the linear kernel are selected as Gaussian process kernel functions, and the corresponding Gaussian process model is initialized. There are 4 Gaussian models for vehicle initialization, which correspond to the position and speed of the vehicle and the position and speed of the preceding vehicle. The present invention selects the state dimension of the current vehicle and the vehicle ahead as the model input, the actual interaction error between the vehicle and the environment as the output, and updates the Gaussian process model by collecting data sets (s t , at , s t +1 ) online, as shown in the figure As shown in Fig. 2, the model of the vehicle dynamics is realized, and the Gaussian distribution of the interaction uncertainty is obtained.
步骤3:基于车载传感器得到本车的当前速度,并通过定义的CLF函数给车辆构造稳定约束,通过拉格朗日乘子法求解上述约束,得到控制量uclf使车辆的速度渐近收敛到设定的期望巡航速度。Step 3: Obtain the current speed of the vehicle based on the on-board sensors, and construct a stable constraint for the vehicle through the defined CLF function, solve the above constraints by the Lagrange multiplier method, and obtain the control variable u clf to make the vehicle's speed asymptotically converge to . Set desired cruise speed.
步骤4:基于期望的最小车间安全距离和车载传感器获得的车辆间距,通过定义的CBF函数给车辆构造安全约束,来实时修正控制量uclf,在最小校正准则下将uclf修正至安全域动作集合内,使车辆的状态空间维持在安全域,并得到最佳动作u*。Step 4: Based on the expected minimum safety distance between vehicles and the distance between vehicles obtained by on-board sensors, construct safety constraints for the vehicle through the defined CBF function to correct the control variable u clf in real time, and correct u clf to the safety domain action under the minimum correction criterion In the set, the state space of the vehicle is maintained in the safe domain, and the optimal action u* is obtained.
步骤5:将车辆获得的动作u*应用于当前车辆的与环境的交互中;Step 5: Apply the action u* obtained by the vehicle to the interaction of the current vehicle with the environment;
步骤6:重复步骤2至6,直至达到期望目标或最大实验设定时长。Step 6: Repeat steps 2 to 6 until the desired goal or maximum experimental set-up time is reached.
在本实施例中,汽车自动巡航系统中,受控车辆能在路面环境和前车加减速引起的不确定性下,能通过本发明提出的算法实现对期望巡航速度的调节以保证安全性,让受控车辆与前车的间距保持在安全距离内。In this embodiment, in the automatic cruise system of the car, the controlled vehicle can adjust the expected cruise speed through the algorithm proposed by the present invention under the uncertainty caused by the road environment and the acceleration and deceleration of the preceding vehicle to ensure safety, Keep the controlled vehicle within a safe distance from the vehicle in front.
显然,本发明的上述实施例仅仅是为清楚地说明本发明所作的举例,而并非是对本发明的实施方式的限定。对于所属领域的普通技术人员来说,在上述说明的基础上还可以做出其它不同形式的变化或变动。这里无需也无法对所有的实施方式予以穷举。凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明权利要求的保护范围之内。Obviously, the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those of ordinary skill in the art, changes or modifications in other different forms can also be made on the basis of the above description. There is no need and cannot be exhaustive of all implementations here. Any modifications, equivalent replacements and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114638346A (en) * | 2022-03-30 | 2022-06-17 | 中国科学院软件研究所 | Posture control device, system and method based on reinforcement learning |
US11938929B2 (en) | 2021-12-15 | 2024-03-26 | Ford Global Technologies, Llc | Obstacle avoidance for vehicle with trailer |
US12233857B2 (en) | 2022-08-10 | 2025-02-25 | Ford Global Technologies, Llc | Obstacle avoidance for vehicle |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6580996B1 (en) * | 2002-08-07 | 2003-06-17 | Visteon Global Technologies, Inc. | Vehicle adaptive cruise control system and method |
CN101417655A (en) * | 2008-10-14 | 2009-04-29 | 清华大学 | Vehicle multi-objective coordinated self-adapting cruise control method |
CN109927725A (en) * | 2019-01-28 | 2019-06-25 | 吉林大学 | A kind of self-adaption cruise system and implementation method with driving style learning ability |
CN110872992A (en) * | 2018-08-30 | 2020-03-10 | 丰田自动车株式会社 | Control device and control method |
CN110928189A (en) * | 2019-12-10 | 2020-03-27 | 中山大学 | Robust control method based on reinforcement learning and Lyapunov function |
CN111231956A (en) * | 2020-02-26 | 2020-06-05 | 江苏大学 | An acceleration constraint control algorithm for vehicle cruise control system |
-
2020
- 2020-06-18 CN CN202010561832.6A patent/CN111897213A/en active Pending
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6580996B1 (en) * | 2002-08-07 | 2003-06-17 | Visteon Global Technologies, Inc. | Vehicle adaptive cruise control system and method |
CN101417655A (en) * | 2008-10-14 | 2009-04-29 | 清华大学 | Vehicle multi-objective coordinated self-adapting cruise control method |
CN110872992A (en) * | 2018-08-30 | 2020-03-10 | 丰田自动车株式会社 | Control device and control method |
CN109927725A (en) * | 2019-01-28 | 2019-06-25 | 吉林大学 | A kind of self-adaption cruise system and implementation method with driving style learning ability |
CN110928189A (en) * | 2019-12-10 | 2020-03-27 | 中山大学 | Robust control method based on reinforcement learning and Lyapunov function |
CN111231956A (en) * | 2020-02-26 | 2020-06-05 | 江苏大学 | An acceleration constraint control algorithm for vehicle cruise control system |
Non-Patent Citations (11)
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11938929B2 (en) | 2021-12-15 | 2024-03-26 | Ford Global Technologies, Llc | Obstacle avoidance for vehicle with trailer |
CN114638346A (en) * | 2022-03-30 | 2022-06-17 | 中国科学院软件研究所 | Posture control device, system and method based on reinforcement learning |
US12233857B2 (en) | 2022-08-10 | 2025-02-25 | Ford Global Technologies, Llc | Obstacle avoidance for vehicle |
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