技术领域technical field
本发明属于智能汽车运动控制领域，具体涉及一种基于驾驶员特性的智能汽车驾驶过程横向动态控制方法。The invention belongs to the field of motion control of intelligent automobiles, and in particular relates to a lateral dynamic control method in the driving process of intelligent automobiles based on driver characteristics.
背景技术Background technique
随着自动驾驶技术的逐渐深入研究，车辆的横向以及纵向控制正在逐渐向更加深入的方向发展，其车辆的横向控制是汽车自主驾驶的关键技术之一。车辆通过横向位置的移动可以帮助车辆进行转弯、避障和换道等一系列的相关操作。以预瞄前方道路信息的预瞄式控制车辆横向位置对路径跟踪有良好的一致性。根据文献以及仿真可知，预瞄时间的选择对路径跟踪的精度、车辆操纵稳定性和乘坐的舒适性有明显的影响。With the gradual in-depth research of autonomous driving technology, the lateral and longitudinal control of vehicles is gradually developing in a more in-depth direction. The lateral control of vehicles is one of the key technologies for autonomous driving. The movement of the vehicle through the lateral position can help the vehicle to perform a series of related operations such as turning, avoiding obstacles and changing lanes. The vehicle's lateral position controlled by previewing the road ahead information has a good consistency for path tracking. According to the literature and simulation, the selection of the preview time has a significant impact on the accuracy of path tracking, vehicle handling stability and ride comfort.
通过仿真试验可以得出，在参考的道路情况下，通过调整合适的预瞄距离可以得到与道路相一致的运行轨迹，且车辆行驶路径的误差与预瞄距离有明显的相关关系。目前的研究中，大多根据仿真调整参数，通过使车辆行驶路径与理想路径相一致，得到最优的预瞄距离。专利CN103439884A以固定的预瞄距离设计智能汽车的预瞄控制方法，该方法虽然能够满足横向控制的精度，但是却存在鲁棒性较差的问题。此外，专利CN107097785A通过设计分层式横向控制器，通过将上下层控制器以车路的整体关系互相迭代得出预瞄距离。该方法只是以道路信息和车辆信息出发，下层控制器以基准滑膜控制，虽然能够实现车辆的路径跟踪，以及预瞄时间的自适应，但是并不能反应实际的驾驶员驾驶车辆的生理过程，不仅如此，在车辆动力学模型一定的情况下，如果在极端驾驶情况下，车辆操纵的稳定性也会有所恶化。根据常识，驾驶员在神经反应时间较长以及肌肉迟滞时间较长时，对车辆的操纵能力也会下降。Through the simulation test, it can be concluded that in the reference road conditions, the running trajectory consistent with the road can be obtained by adjusting the appropriate preview distance, and the error of the vehicle's driving path has a clear correlation with the preview distance. In the current research, most of the parameters are adjusted according to the simulation, and the optimal preview distance is obtained by making the vehicle's driving path consistent with the ideal path. Patent CN103439884A designs a preview control method for smart cars with a fixed preview distance. Although this method can meet the accuracy of lateral control, it has the problem of poor robustness. In addition, the patent CN107097785A designs a layered lateral controller, and obtains the preview distance by iterating the upper and lower controllers based on the overall relationship between vehicles and roads. This method is only based on road information and vehicle information, and the lower controller is controlled by the reference synovium. Although it can realize vehicle path tracking and self-adaptation of preview time, it cannot reflect the actual physiological process of the driver driving the vehicle. Not only that, in the case of a certain vehicle dynamics model, the stability of vehicle handling will also deteriorate in extreme driving situations. According to common sense, the driver's ability to control the vehicle will also decrease when the nerve reaction time is longer and the muscle lag time is longer.
发明内容Contents of the invention
本发明的目的是提出一种基于驾驶员特性的智能汽车驾驶过程横向动态控制方法。The purpose of the invention is to propose a lateral dynamic control method of an intelligent car driving process based on driver characteristics.
7.本发明的目的是通过以下技术方案来实现的：本发明提供一种基于驾驶员特性的智能汽车驾驶过程横向动态控制方法，该方法包括以下步骤：7. the object of the present invention is achieved by the following technical solutions: the present invention provides a kind of intelligent vehicle driving process lateral dynamic control method based on driver characteristics, the method may further comprise the steps:
步骤1.建立二自由度的车辆动力学模型作为参考模型Gv(s)；Step 1. Establish a two-degree-of-freedom vehicle dynamics model as the reference model Gv (s);
步骤2.构建反应驾驶员操作特性的驾驶员模型；Step 2. Build a driver model that reflects the driver's operating characteristics;
步骤3.构建预瞄环节P(s)；Step 3. Build the preview link P(s);
步骤4.根据参考模型Gv(s)、驾驶员模型Gp(s)、预瞄环节P(s)构建闭环控制系统；Step 4. Build a closed-loop control system according to the reference model Gv (s), the driver model Gp (s), and the preview link P (s);
步骤5.用零阶保持器，对所构建的闭环控制系统离散化；Step 5. Discretize the constructed closed-loop control system with a zero-order keeper;
步骤6.建立最小二乘辨识函数；Step 6. Establishing a least squares identification function;
步骤7.以最小化车辆自身位置的损失函数为目标，根据延迟时间和惯性时间确定预瞄时间，通过预瞄时间确定的预瞄距离d；再迭代算出延迟时间和惯性时间，以达到动态确定各个参数的目的。Step 7. With the goal of minimizing the loss function of the vehicle's own position, determine the preview time according to the delay time and inertia time, and determine the preview distance d through the preview time; then iteratively calculate the delay time and inertia time to achieve dynamic determination purpose of each parameter.
优选地，在步骤1中，所述建立二自由度的车辆动力学模型Gv(s)具体为：Preferably, in step 1, the vehicle dynamics model Gv (s) of said establishment of two degrees of freedom is specifically:
式中；ω为车辆横摆角速度，分别为车辆横向加速度、车辆横摆角加速度、车辆的横向速度、车辆横摆角，其中Izz为车轮横摆角转动惯量；vx、vy分别为车辆的纵向速度和横向速度；ψ为车辆横摆角；y为车辆在大地坐标系下的横向位移；θsw为方向盘转角；nrsw为方向盘转角到前轮转角的传动比，车轮转角θf＝θsw/nrsw。where ω is the yaw rate of the vehicle, are the vehicle lateral acceleration, vehicle yaw angular acceleration, vehicle lateral velocity, and vehicle yaw angle, respectively, where Izz is the moment of inertia of the wheel yaw angle; vx and vy are the longitudinal velocity and lateral velocity of the vehicle respectively; ψ is the yaw angle of the vehicle; y is the lateral displacement of the vehicle in the earth coordinate system; θsw is the steering wheel angle; nrsw is the transmission ratio from the steering wheel angle to the front wheel angle, and the wheel angle θf =θsw /nrsw .
优选地，在所述步骤2中，所述的驾驶员模型为：Preferably, in said step 2, said driver model is:
Td、Th分别为延迟时间和性迟滞时间。 Td ,Th h are delay time and hysteresis time, respectively.
优选地，在所述步骤3中，所述的预瞄环节P(s)为：Tp为预瞄时间。Preferably, in the step 3, the preview link P(s) is: Tp is the preview time.
优选地，所述闭环控制系统的传递函数Gt(s)具体为:Preferably, the transfer function Gt (s) of the closed-loop control system is specifically:
式中：In the formula:
优选地，所述最小二乘辨识函数为，Preferably, the least squares identification function is,
Tp＝ω0+ω1Td+ω2Th+ω3vx+ω4vy+ω5(1R)Tp ＝ω0 +ω1 Td +ω2 Th +ω3 vx +ω4 vy +ω5 (1R)
式中：Tp、Td、Th、vx、vy、R分别为预瞄时间、延迟时间、惯性时间、横向速度、纵向速度、转弯半径；ω0、ω1、ω2、ω3、ω4、ω5为待辨识参数，通过下面的最小二乘估计函数C得到；In the formula: Tp , Td , Th , vx , vy , R are preview time, delay time, inertia time, lateral velocity, longitudinal velocity, and turning radius respectively; ω0 , ω1 , ω2 , ω3 , ω4 , ω5 are the parameters to be identified, which are obtained by the following least square estimation function C;
式中：Td*为期望延迟时间、Th*为惯性时间、vx*为横向速度、vy*为纵向速度、1/R为曲率，C为最小二乘估计函数，可以计算出ω0、ω1、ω2、ω3、ω4、ω5；Td、Th、vx、vy、R由传感器采集直接或者间接通过计算得出；Td*、Th*、vx*、vy*、1/R*通过实际驾驶数据得出。In the formula: Td* is the expected delay time, Th* is the inertia time, vx* is the lateral velocity, vy* is the longitudinal velocity, 1/R is the curvature, C is the least square estimation function, and ω can be calculated0 , ω1 , ω2 , ω3 , ω4 , ω5 ; Td , Th , vx , vy , R are directly or indirectly calculated from sensor collection; Td* , Th* , vx* ,vy* , 1/R* are obtained from actual driving data.
由于采用了上述技术方案，本发明具有如下的优点：Owing to adopting above-mentioned technical scheme, the present invention has following advantage:
本发明提出一种基于驾驶员特性的智能汽车驾驶过程横向动态控制方法，该方法汽车动力学模型的基础之上，加入驾驶员的预瞄时间、延迟时间和惯性迟滞等特征来模拟驾驶员的实际操作过程，该操作过程反应实际驾驶员驾驶车辆时的生理条件，并通过驾驶过程时，车的横向速度、纵向速度、转弯曲率等参数来调整预瞄时间，同时，建立预瞄时间和延迟时间、惯性迟滞时间的关系，动态的调整预瞄时间，既保证模型在路径跟随时的一致性，同时又提高了乘坐舒适性。The present invention proposes a lateral dynamic control method in the driving process of an intelligent car based on the characteristics of the driver. On the basis of the vehicle dynamics model, features such as the driver's preview time, delay time and inertia hysteresis are added to simulate the driver's behavior. The actual operation process, which reflects the physiological conditions of the actual driver when driving the vehicle, and adjusts the preview time through parameters such as the lateral speed, longitudinal speed, and curvature of the car during the driving process, and at the same time, establishes the preview time and delay The relationship between time and inertial lag time, and the dynamic adjustment of the preview time not only ensure the consistency of the model when following the path, but also improve the ride comfort.
本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述，并且在某种程度上，基于对下文的考察研究对本领域技术人员而言将是显而易见的，或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。Other advantages, objects and features of the present invention will be set forth in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the investigation and research below, or can be obtained from It is taught in the practice of the present invention. The objects and other advantages of the invention may be realized and attained by the following specification.
附图说明Description of drawings
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步的详细描述：In order to make the purpose of the present invention, technical solutions and advantages clearer, the present invention will be described in further detail below in conjunction with accompanying drawing:
图1是基于驾驶员特性的智能汽车驾驶过程横向动态控制流程图；Fig. 1 is a flow chart of lateral dynamic control of intelligent car driving process based on driver characteristics;
图2是整车二自由度车辆模型示意图；Fig. 2 is a schematic diagram of a two-degree-of-freedom vehicle model of the whole vehicle;
图3是车辆与路径的集合关系；Fig. 3 is the collection relation of vehicle and path;
图4是人-车-路闭环系统结构框图。Figure 4 is a structural block diagram of the human-vehicle-road closed-loop system.
具体实施方式Detailed ways
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention.
请参阅图1至图4。需要说明的是，本实施例中所提供的图示仅以示意方式说明本发明的基本构想，遂图式中仅显示与本发明中有关的组件而非按照实际实施时的组件数目、形状及尺寸绘制，其实际实施时各组件的型态、数量及比例可为一种随意的改变，且其组件布局型态也可能更为复杂。See Figures 1 through 4. It should be noted that the diagrams provided in this embodiment are only schematically illustrating the basic idea of the present invention, and only the components related to the present invention are shown in the diagrams rather than the number, shape and shape of the components in actual implementation. Dimensional drawing, the type, quantity and proportion of each component can be changed arbitrarily during actual implementation, and the component layout type may also be more complicated.
为了克服上述问题，本发明需要提出一种基于驾驶员特性的智能汽车驾驶过程横向动态控制方法，既要保证模型在路径跟随时的一致性，同时又要提高乘坐舒适性。In order to overcome the above-mentioned problems, the present invention needs to propose a lateral dynamic control method of the driving process of an intelligent vehicle based on the characteristics of the driver, which not only ensures the consistency of the model during path following, but also improves the ride comfort.
为实现上述目标，本发明的技术方案是：一种基于驾驶员特性的智能汽车驾驶过程横向动态控制方法，包括以下步骤：In order to achieve the above object, the technical solution of the present invention is: a method for lateral dynamic control of the driving process of an intelligent car based on driver characteristics, comprising the following steps:
步骤1，首先建立二自由度的车辆动力学模型作为参考模型Gv(s)，驾驶员在驾驶过程中并不能理解复杂的车辆动力学模型，而是靠驾驶经验使得汽车保持在道路的合适位置，二自由度的驾驶员模型能充分反映车辆的横向动力学状态，模型如下：Step 1, first establish a two-degree-of-freedom vehicle dynamics model as a reference model Gv (s), the driver cannot understand the complex vehicle dynamics model during driving, but relies on driving experience to keep the car on the road. position, the two-degree-of-freedom driver model can fully reflect the lateral dynamic state of the vehicle, and the model is as follows:
式中：m为整车质量；Cf、Cr分别为前后轮等效侧偏刚度；lf、lr分别为整车质心到前后轮的距离；Izz为车轮横摆角转动惯量；vx、vy分别为车辆的纵向速度和横向速度；ψ为车辆横摆角；y为车辆在大地坐标系下的横向位移；θsw为方向盘转角；nrsw为方向盘转角到前轮转角的传动比，车轮转角θf＝θsw/nrsw；In the formula: m is the mass of the vehicle; Cf and Cr are the equivalent cornering stiffness of the front and rear wheels respectively; lf and lr are the distances from the center of mass of the vehicle to the front and rear wheels respectively; Izz is the moment of inertia of the wheel yaw angle; vx , vy are the longitudinal speed and lateral speed of the vehicle respectively; ψ is the yaw angle of the vehicle; y is the lateral displacement of the vehicle in the earth coordinate system; θsw is the steering wheel angle; nrsw is the distance from the steering wheel angle to the front wheel angle Transmission ratio, wheel angle θf = θsw /nrsw ;
以上参数单位分别为kg、N/rad、m、kg·m2、m/s、rad/s、m、rad。The units of the above parameters are respectively kg, N/rad, m, kg·m2 , m/s, rad/s, m, rad.
步骤2，构建反应驾驶员操作特性的驾驶员模型，主要包括神经反应延迟环节、肌肉迟滞环节Step 2, build a driver model that reflects the driver's operating characteristics, mainly including the nerve response delay link and the muscle hysteresis link
式中：Td、Th分别为延迟时间和惯性时间。In the formula: Td ,Th h are delay time and inertia time respectively.
步骤3，构建预瞄环节P(s)，Step 3, build the preview link P(s),
式中：Tp为预瞄时间。In the formula: Tp is the preview time.
根据车辆与参考路径的位置关系如图3所示，计算预瞄点处的横向误差f(t+T)-y(t)及方向误差(预瞄点与速度方向的夹角)汽车的运动简单服从Acklman关系，汽车轨迹曲率与转向盘转角成正比：According to the positional relationship between the vehicle and the reference path as shown in Figure 3, calculate the lateral error f(t+T)-y(t) and direction error (the angle between the preview point and the direction of velocity) at the preview point The motion of the car simply obeys the Acklman relationship, and the curvature of the car trajectory is proportional to the steering wheel angle:
或 or
式中：R为转弯半径；L为轴距，当驾驶过程中根据道路的曲率需要改变方向盘转角时，就会适当的产生响应的转向操作。In the formula: R is the turning radius; L is the wheelbase. When the steering wheel angle needs to be changed according to the curvature of the road during driving, a responsive steering operation will be generated appropriately.
或 or
y(t),分别表示期望的横向加速度、横向位移、横向速度。 y(t), Respectively represent the desired lateral acceleration, lateral displacement, and lateral velocity.
得到最优转向盘转角θsw*与预瞄距离d的关系：Get the relationship between the optimal steering wheel angle θsw* and the preview distance d:
式中：f(t)为当前位置信息；f(t+T)为当前点预瞄时间T后的位置信息。In the formula: f(t) is the current position information; f(t+T) is the position information after the preview time T of the current point.
步骤4，将步骤1～3组合成一个闭环控制系统如图4所示。具体地传递函数Gt(s)为：Step 4, combine steps 1 to 3 into a closed-loop control system as shown in Figure 4. Specifically, the transfer function Gt (s) is:
式中：In the formula:
步骤5，用零阶保持器，对所构造的整个控制系统离散化。Step 5, discretize the entire control system constructed by using the zero-order holder.
Ga(z)＝Z[H(s)Ga(s)]Ga (z)=Z[H(s)Ga (s)]
式中：Tc为采样时间；Ga(s)为整体的传递函数；Ga(z)为离散的之后的传递函数。状态空间表达式用零阶保持器转换为离散化之后系统系统的状态方程为：In the formula: Tc is the sampling time; Ga (s) is the transfer function of the whole; Ga (z) is the transfer function after discrete. The state equation of the system after the state space expression is transformed into discretization by the zero-order holder is:
k表示离散时间步长，x(k)表示系统状态变量，u(k)为输入量，y(k)为观测量，Ad为状态矩阵，Bd为控制矩阵，Cd为输出矩阵，Dd为直接传递矩阵。k represents the discrete time step, x(k) represents the system state variable, u(k) is the input quantity, y(k) is the observation quantity, Ad is the state matrix, Bd is the control matrix, Cd is the output matrix, Dd is the direct transfer matrix.
步骤6，建立最小二乘辨识函数Step 6, establish the least squares identification function
Tp＝ω0+ω1Td+ω2Th+ω3vx+ω4vy+ω5(1/R)Tp ＝ω0 +ω1 Td +ω2 Th +ω3 vx +ω4 vy +ω5 (1/R)
式中：Tp、Td、Th、vx、vy、R分别为预瞄时间、延迟时间、惯性时间、横向速度、纵向速度、转弯半径；ω0、ω1、ω2、ω3、ω4、ω5为待辨识参数，可以通过下面的最小二乘估计函数得到。In the formula: Tp , Td , Th , vx , vy , R are preview time, delay time, inertia time, lateral velocity, longitudinal velocity, and turning radius respectively; ω0 , ω1 , ω2 , ω3 , ω4 , ω5 are the parameters to be identified, which can be obtained by the following least square estimation function.
将期望延迟时间Td*、惯性时间Th*、横向速度vx*、纵向速度vy*、曲率1/R与待辨识的参数的平方和最小，即The sum of the squares of the expected delay time Td* , inertia time Th* , lateral velocity vx* , longitudinal velocity vy* , curvature 1/R and the parameters to be identified is minimized, namely
式中：C为最小二乘估计函数，可以计算出ω0、ω1、ω2、ω3、ω4、ω5；Td、Th、vx、vy、R由传感器采集直接或者间接通过简单计算得出；Td*、Th*、vx*、vy*、1/R*可通过实际驾驶数据得出。In the formula: C is the least square estimation function, which can calculate ω0 , ω1 , ω2 , ω3 , ω4 , ω5 ; Td , Th , vx , vy , R are directly collected by sensors or Indirectly obtained by simple calculation; Td* , Th* , vx* , vy* , 1/R* can be obtained by actual driving data.
由车辆自身位置的损失函数The loss function by the vehicle's own position
式中：yp(k)、ψp(k)、θp.sw(k)分别为预瞄点的横向位置、预瞄点的偏航角、预期的方向盘转角；λ1,λ2,λ3分别为预瞄点的横向位置的权值、预瞄点的偏航角的权值、预期的方向盘转角的权值，可以通过使车辆自身位置的损失函数最小求得。In the formula: yp (k), ψp (k), and θp.sw (k) are respectively the lateral position of the preview point, the yaw angle of the preview point, and the expected steering wheel angle; λ1 , λ2 , λ3 is the weight of the lateral position of the preview point, the weight of the yaw angle of the preview point, and the weight of the expected steering wheel angle, which can be obtained by minimizing the loss function of the vehicle's own position.
通过计算车辆位置损失函数使其最小再返回去验证延迟时间Td、惯性时间Th得出的预瞄时间是否满足道路行驶要求。具体地，By calculating the vehicle position loss function to make it the minimum and then returning to verify whether the preview time obtained by the delay time Td and the inertia timeTh h meets the road driving requirements. specifically,
通过前述的最小二乘函数，已经确定Tp＝ω0+ω1Td+ω2Th+ω3vx+ω4vy+ω5(1/R)中的参数，就可以求出预瞄时间Tp，继而可以计算出y(k)，与损失函数J求出的yp(k)做比较，判断是否满足要求。其中y(k)、θsw(k)与Tp、vy、R有如下关系。Through the aforementioned least squares function, the parameters in Tp = ω0 + ω1 Td + ω2 Th + ω3 vx + ω4 vy + ω5 (1/R) have been determined, then we can find Get the preview time Tp , and then calculate y(k), compare it with the yp (k) calculated by the loss function J, and judge whether it meets the requirements. Among them, y(k), θsw (k) and Tp , vy , R have the following relations.
y(k)＝Tp·vyy(k)=Tp ·vy
步骤7，以最小化车辆自身位置的损失函数为目标，根据延迟时间和惯性时间确定预瞄时间，通过预瞄时间确定的预瞄距离d；再迭代算出延迟时间和惯性时间，以达到动态确定各个参数的目的。Step 7, with the goal of minimizing the loss function of the vehicle's own position, determine the preview time according to the delay time and inertia time, and determine the preview distance d through the preview time; then iteratively calculate the delay time and inertia time to achieve dynamic determination purpose of each parameter.
d＝Tp·vxd=Tp vx
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本技术方案的宗旨和范围，其均应涵盖在本发明的保护范围当中。Finally, it is noted that the above embodiments are only used to illustrate the technical solutions of the present invention without limitation. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be carried out Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should be included in the protection scope of the present invention.
| Application Number | Priority Date | Filing Date | Title |
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| CN201810550220.XACN108791301B (en) | 2018-05-31 | 2018-05-31 | Intelligent automobile driving process transverse dynamic control method based on driver characteristics |
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| CN201810550220.XACN108791301B (en) | 2018-05-31 | 2018-05-31 | Intelligent automobile driving process transverse dynamic control method based on driver characteristics |
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| CN108791301Atrue CN108791301A (en) | 2018-11-13 |
| CN108791301B CN108791301B (en) | 2020-03-24 |
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| CN201810550220.XAActiveCN108791301B (en) | 2018-05-31 | 2018-05-31 | Intelligent automobile driving process transverse dynamic control method based on driver characteristics |
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