CN116749700A - A vehicle active suspension control method that considers occupant motion sickness based on road surface information - Google Patents
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Abstract
本发明涉及一种基于路面信息考虑乘员晕动的车辆主动悬架控制方法，包括以下步骤：建立车‑座椅主动悬架耦合动力学模型；使用双目视觉识别算法对车辆行驶过程中的随机路面信息进行识别，并且对路面信息进行等级划分，利用预瞄控制算法，将路面等级信息输入到车‑椅主动悬架系统模型中，作为激励信号输入；使用模型预测控制(MPC)算法对主动悬架性能指标进行滚动优化，降低主动悬架系统的垂向加速度值，充分考虑车辆悬架的垂向加速度产生车辆垂向振动进而造成乘员晕动症发生的影响，将研究的关键性能指标分别进行控制，通过改进算法来改善车辆的垂向加速度大小，最终有效改善自动驾驶车辆的平顺性和操纵稳定性，降低了车内乘员晕动症的发生率。
The invention relates to a vehicle active suspension control method that considers occupant motion sickness based on road surface information. It includes the following steps: establishing a vehicle-seat active suspension coupling dynamic model; using a binocular vision recognition algorithm to detect random changes in the vehicle's driving process. The road surface information is identified and graded. The preview control algorithm is used to input the road surface grade information into the car-seat active suspension system model as an excitation signal input; the model predictive control (MPC) algorithm is used to control the active suspension system. Suspension performance indicators are rolled to optimize, reduce the vertical acceleration value of the active suspension system, fully consider the impact of the vertical acceleration of the vehicle suspension causing vehicle vertical vibration and thus causing occupant motion sickness. The key performance indicators studied are respectively Control is performed to improve the vertical acceleration of the vehicle through improved algorithms, ultimately effectively improving the ride comfort and handling stability of the autonomous vehicle and reducing the incidence of motion sickness among vehicle occupants.
Description
技术领域Technical field
本发明涉及自动驾驶汽车的智能控制技术领域，尤其涉及一种基于路面信息考虑乘员晕动的车辆主动悬架控制方法。The present invention relates to the technical field of intelligent control of autonomous vehicles, and in particular to a vehicle active suspension control method that considers occupant motion sickness based on road surface information.
背景技术Background technique
目前，随着智能车辆技术的发展，汽车自动驾驶技术得到了很大的提升，自动驾驶车辆的乘坐舒适性和操纵稳定性也直接关系到了自动驾驶技术的推广和应用。由于在自动驾驶车辆行驶过程中驾驶员角色改变，车内乘员在车辆运行过程中一般会进行一系列的休闲活动，然而在车辆运行过程中乘员晕动症的发生会导致乘员身体不舒适，乘车体验感变差。所以，如何改善自动驾驶车辆行驶的稳定性和提高车辆的平顺性可以有效的改善乘员的晕车效果，降低晕车率，这是目前自动驾驶行业中亟待解决的问题。At present, with the development of intelligent vehicle technology, automatic driving technology has been greatly improved. The ride comfort and control stability of automatic driving vehicles are also directly related to the promotion and application of automatic driving technology. Since the role of the driver changes during the operation of an autonomous vehicle, the occupants in the vehicle generally engage in a series of leisure activities during the operation of the vehicle. However, the occurrence of motion sickness among the occupants during the operation of the vehicle will cause physical discomfort to the occupants. The car experience becomes worse. Therefore, how to improve the driving stability of autonomous vehicles and improve the ride comfort of the vehicle can effectively improve the motion sickness effect of passengers and reduce the motion sickness rate. This is an urgent problem that needs to be solved in the current autonomous driving industry.
线控底盘技术的发展给自动驾驶车辆平顺性和操稳性的提高带来了可能，目前线控底盘技术不断推广，成为了越来越多中高级汽车的标配。基于线控底盘的特点，车辆主动悬架技术也得到了广泛的应用，主动悬架能够基于路面信息的输入自适应的调整悬架的阻尼特性，提高了车辆的平顺性。乘员晕车症的发生除了受车辆横纵向加速度的影响外，也受车辆垂向加速度的影响。假如能够在车辆行驶过程中，减小车辆的垂向加速度，则可以有效的减小乘员晕车率，所以主动悬架的使用对于自动驾驶车辆技术的改进有着重要的作用。The development of drive-by-wire chassis technology has made it possible to improve the ride comfort and handling stability of autonomous vehicles. Currently, drive-by-wire chassis technology continues to be promoted and has become a standard feature of more and more mid- to high-end cars. Based on the characteristics of drive-by-wire chassis, vehicle active suspension technology has also been widely used. Active suspension can adaptively adjust the damping characteristics of the suspension based on the input of road information, improving the ride comfort of the vehicle. The occurrence of motion sickness among passengers is not only affected by the horizontal and longitudinal acceleration of the vehicle, but also by the vertical acceleration of the vehicle. If the vertical acceleration of the vehicle can be reduced while the vehicle is driving, the motion sickness rate of the occupants can be effectively reduced. Therefore, the use of active suspension plays an important role in the improvement of autonomous vehicle technology.
在迅猛发展的智能汽车领域，激光雷达、双目相机等车载传感器被用于为汽车提供更加丰富的感知信息。感知技术的发展，使得智能汽车有了属于自己的“眼睛”，能够精确判断周围的事物，感知技术的应用对自动驾驶汽车的发展起着不可替代的作用。利用视觉识别技术感知到的车辆行驶路面不平度信息加入到主动悬架优化算法设计中，可以充分利用视觉感知实现悬架系统预瞄控制，有效改善车辆的平顺性和操纵稳定性，从而增加车辆的舒适性。目前智能车辆在行驶过程中无法基于路面特征，实时改善车辆行驶过程中的平顺性，进一步改善车辆的驾乘舒适性，导致车内乘员晕动症发生的概率增加。In the rapidly developing field of smart cars, on-board sensors such as lidar and binocular cameras are used to provide cars with richer sensory information. The development of perception technology has given smart cars their own "eyes" that can accurately judge the surrounding things. The application of perception technology plays an irreplaceable role in the development of self-driving cars. Using visual recognition technology to sense the unevenness of the vehicle's driving road surface is added to the design of the active suspension optimization algorithm. Visual perception can be fully utilized to achieve preview control of the suspension system, effectively improving the ride comfort and handling stability of the vehicle, thereby increasing the vehicle's comfort. At present, smart vehicles cannot improve the ride comfort of the vehicle in real time based on road surface characteristics during driving, and further improve the driving comfort of the vehicle, resulting in an increased probability of motion sickness among vehicle occupants.
发明内容Contents of the invention
本发明的目的在于提供一种基于路面信息考虑乘员晕动的车辆主动悬架控制方法，针对自动驾驶汽车在行驶过程中容易使得乘客发生晕车这一问题，通过使用双目视觉识别技术识别路面信息，将路面信息输入到车-椅主动悬架系统中，车载控制器能够根据路面不平度信号，及时调整车-椅主动悬架系统的阻尼特性，使得车辆行驶到对应的路面上时，能够有最佳的舒适性。The purpose of the present invention is to provide a vehicle active suspension control method that considers occupant motion sickness based on road surface information. Aiming at the problem that autonomous vehicles easily cause passengers to experience motion sickness during driving, binocular vision recognition technology is used to identify road surface information. , input the road surface information into the vehicle-seat active suspension system, and the vehicle-mounted controller can timely adjust the damping characteristics of the vehicle-seat active suspension system according to the road unevenness signal, so that when the vehicle drives on the corresponding road surface, it can Optimal comfort.
本发明采用的技术方案如下：The technical solutions adopted by the present invention are as follows:
本发明所提出的一种基于路面信息考虑乘员晕动的车辆主动悬架控制方法，包括以下步骤：The invention proposes a vehicle active suspension control method that considers occupant motion sickness based on road surface information, including the following steps:
S1、建立车-座椅主动悬架系统动力学模型；通过分析该模型能够得出在路面信息的输入下，主动悬架能够随着路面信息的不同实时调整主动悬架中CDC减振器的阻尼大小；S1. Establish a vehicle-seat active suspension system dynamics model; by analyzing this model, it can be concluded that with the input of road information, the active suspension can adjust the CDC shock absorber in the active suspension in real time according to the different road information. Damping size;
S2、在车辆前侧上方利用双目摄像头对路面信息进行提取，并使用双目视觉识别算法对车辆行驶过程中的随机路面信息进行识别，并且对路面信息进行等级划分，利用预瞄控制算法，将路面等级信息输入到车-座椅主动悬架系统模型中，作为激励信号输入；S2. Use a binocular camera above the front side of the vehicle to extract road information, use a binocular visual recognition algorithm to identify random road information while the vehicle is driving, classify the road information into grades, and use a preview control algorithm. Input road surface grade information into the vehicle-seat active suspension system model as an excitation signal input;
S3、建立晕车模型系统感知车辆运动状态，包括车辆的运动激励信号，以及座椅悬架给乘员身体的激励信号；S3. Establish a motion sickness model system to sense the vehicle's motion status, including the vehicle's motion excitation signal and the excitation signal from the seat suspension to the occupant's body;
S4、使用模型预测控制(MPC)算法对主动悬架性能指标进行滚动优化，降低主动悬架系统的垂向加速度值，充分考虑车辆悬架的垂向加速度产生车辆垂向振动进而造成乘员晕动症发生的影响，将关键性能指标分别进行控制，通过改进算法来改善车辆的垂向加速度大小，最终有效改善自动驾驶车辆的平顺性和操纵稳定性。S4. Use the model predictive control (MPC) algorithm to perform rolling optimization of active suspension performance indicators, reduce the vertical acceleration value of the active suspension system, and fully consider the vertical acceleration of the vehicle suspension to produce vertical vibration of the vehicle and thereby cause motion sickness of the occupants. According to the impact of the disease, the key performance indicators are controlled separately, and the vertical acceleration of the vehicle is improved by improving the algorithm, which ultimately effectively improves the ride comfort and handling stability of the autonomous vehicle.
进一步的，所述步骤S1中，车-座椅主动悬架系统动力学模型的微分方程如下：Further, in step S1, the differential equation of the vehicle-seat active suspension system dynamics model is as follows:
式中：md为簧下质量，mc为簧上质量，mb为人与车辆座椅总质量，zg为路面的输入位移，zb为座椅的位移，zc为车身的位移，zd为轮胎的位移，kb、kc、kd分别为对应弹簧的阻尼系数，F1、F2为控制力；In the formula: m d is the unsprung mass, m c is the sprung mass, m b is the total mass of the person and the vehicle seat, z g is the input displacement of the road surface, z b is the displacement of the seat, z c is the displacement of the body, z d is the displacement of the tire, k b , k c , and k d are the damping coefficients of the corresponding springs respectively, and F1 and F2 are the control forces;
将上述微分方程写为状态空间方程可以得出：Writing the above differential equation as a state space equation gives:
得到状态矢量z(t),状态矩阵A，控制输入U(t)，控制输入矩阵B，噪声输入矩阵F，高斯白噪声W(t)；Get the state vector z(t), state matrix A, control input U(t), control input matrix B, noise input matrix F, and Gaussian white noise W(t);
U(t)＝[F1 F2]T，W(t)＝[wt]。 U(t)=[F 1 F 2 ] T , W(t)=[w t ].
进一步的，所述步骤S4具体包括：对步骤S1推导得出的车-座椅主动悬架系统动力学模型进行MPC算法设计，通过MPC算法采用离散模型预测被控对象的未来状态，并且通过求解有限时域内的最优化问题得到最优控制量，将连续的车辆主动悬架动力学方程离散化：Further, the step S4 specifically includes: performing MPC algorithm design on the vehicle-seat active suspension system dynamics model derived in step S1, using the MPC algorithm to use the discrete model to predict the future state of the controlled object, and solving the The optimal control quantity is obtained from the optimization problem in the finite time domain, and the continuous vehicle active suspension dynamic equation is discretized:
式中：T为控制步长；x(k|k)，ω(k|k)为第K时刻的测量量，代表在k时刻的真实系统状态和路面激励；y(k|k)，u(k|k)和x(k+1|k)为k时刻的预测量，代表在k时刻预测的k时刻系统输出，k时刻控制量和k+1时刻系统状态；In the formula: T is the control step size; x(k|k), ω(k|k) are the measurement quantities at the Kth moment, representing the real system state and road excitation at the k moment; y(k|k), u(k| k) and x(k+1|k) are the predicted quantities at moment k, representing the system output at moment k predicted at moment k, the control quantity at moment k and the system state at moment k+1;
进一步的，为了保证车辆具有良好的平顺性和操纵稳定性，设定MPC控制器优化问题的目标函数为min J(y,u)，即使得系统的输出和阻尼力尽可能小，减小路面激励给人体带来的冲击；Furthermore, in order to ensure that the vehicle has good ride comfort and handling stability, the objective function of the MPC controller optimization problem is set to min J(y,u), that is, to make the output and damping force of the system as small as possible and reduce the road surface The impact of stimulation on the human body;
式中：Q和R分别为权重矩阵。In the formula: Q and R are weight matrices respectively.
进一步的，所述步骤S2中，在双目摄像头对路面信息提取过程中，利用大量的含有不同路面信息的图片组建成数据库，基于VGGNet结构，整个网络都使用同样大小的卷积核和最大池化尺寸，利用VGG16神经网络对数据库中图片加入标签并进行训练学习；按照国际标准化不同路况下的时域扰动曲线不同，并以路面PSD的形式进行描述；通常采用下式拟合路面激励的功率谱：Further, in step S2, during the process of extracting road surface information by binocular cameras, a large number of pictures containing different road surface information are used to form a database. Based on the VGGNet structure, the entire network uses the same size convolution kernel and maximum pooling. Size, use VGG16 neural network to add labels to the images in the database and perform training and learning; according to international standardization, the time domain disturbance curves under different road conditions are different and described in the form of road PSD; the following formula is usually used to fit the power of road excitation Spectrum:
其中：空间频率为n(m-1)；参考空间频率为n0；通常取n0＝0.1(m-1)；路面不平度系数为Gq(n0)(m3)，频率指数一般选为2。Among them: the spatial frequency is n (m -1 ); the reference spatial frequency is n 0 ; usually n 0 = 0.1 (m -1 ); the road roughness coefficient is G q (n 0 ) (m 3 ), and the frequency index is general Select 2.
进一步的，所述晕车模型系统包括乘员身体激励感知模块、车辆状态感知模块和用户乘车舒适性反馈模块；所述车辆状态感知模块用于感知车辆的运动状态，包括车辆的运动激励信号；所述乘员身体激励感知模块用于感知座椅悬架给乘员身体的激励信号；所述用户乘车舒适性反馈模块用于根据乘员的主观感受调节车辆运行的模式，及时切换车辆悬架系统运行模式。Further, the motion sickness model system includes an occupant body excitation sensing module, a vehicle state sensing module and a user ride comfort feedback module; the vehicle state sensing module is used to sense the motion state of the vehicle, including the vehicle's motion excitation signal; The passenger body excitation sensing module is used to sense the excitation signal from the seat suspension to the passenger's body; the user ride comfort feedback module is used to adjust the vehicle operation mode according to the passenger's subjective feelings and timely switch the vehicle suspension system operation mode. .
本发明与现有技术相比具有以下有益效果：Compared with the prior art, the present invention has the following beneficial effects:
本发明是针对目前智能车辆不能够通过车辆行驶过程中的路面信息，实时改善车辆行驶过程中的平顺性，进而改善车辆的驾乘舒适性。与传统提升车辆平顺性的方法相比，本发明方法在提高汽车平顺性的基础上，结合实时路面信息并且将主动悬架垂向加速度作为优化目标进行实时优化，有效提高了车内乘员实际的乘坐舒适性。之前的发明中大多数基于车辆前轴预瞄，后轴减振来设计。本发明直接通过视觉识别技术提取车前路面信息作为目标输入，通过优化后可以达到最优的减振效果，减小乘员晕动症发生的概率。The present invention is aimed at the fact that current smart vehicles cannot improve the ride comfort of the vehicle in real time through the road surface information during the vehicle's driving, thereby improving the driving comfort of the vehicle. Compared with the traditional method of improving vehicle ride comfort, the method of the present invention combines real-time road surface information and takes the active suspension vertical acceleration as the optimization target for real-time optimization on the basis of improving vehicle ride comfort, effectively improving the actual safety of the vehicle occupants. Ride comfort. Most of the previous inventions were designed based on vehicle front axle preview and rear axle vibration reduction. This invention directly extracts the road surface information in front of the vehicle as target input through visual recognition technology. After optimization, it can achieve the optimal vibration reduction effect and reduce the probability of motion sickness in the occupants.
附图说明Description of the drawings
图1是本发明方法的原理示意图；Figure 1 is a schematic diagram of the principle of the method of the present invention;
图2是本发明中车-座椅主动悬架系统的工作原理示意图；Figure 2 is a schematic diagram of the working principle of the vehicle-seat active suspension system of the present invention;
图3是双目摄像头采集路面信息的示意图；Figure 3 is a schematic diagram of a binocular camera collecting road information;
图4是不同等级的路面激励信号示意图；Figure 4 is a schematic diagram of different levels of road excitation signals;
图5晕动产生示意图。Figure 5 Schematic diagram of motion sickness.
具体实施方式Detailed ways
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图做以简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that need to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.
本发明所提出的一种基于路面信息考虑乘员晕动的车辆主动悬架控制方法，如图1-5所示，具体实施过程如下：The present invention proposes a vehicle active suspension control method that considers occupant motion sickness based on road surface information, as shown in Figures 1-5. The specific implementation process is as follows:
S1、建立车-座椅主动悬架系统动力学模型；其中，车-座椅主动悬架系统包括车身质量模块、可变阻尼减振器(CDC)、簧下质量模块、簧上质量模块、传感器、ECU电控单元、驱动电机、空气弹簧等；S1. Establish a vehicle-seat active suspension system dynamics model; among which, the vehicle-seat active suspension system includes a body mass module, a variable damping damper (CDC), an unsprung mass module, a sprung mass module, Sensors, ECU electronic control units, drive motors, air springs, etc.;
所述车-座椅主动悬架系统动力学模型的微分方程如下：The differential equation of the vehicle-seat active suspension system dynamics model is as follows:
式中：md为簧下质量，mc为簧上质量，mb为人与车辆座椅总质量，zg为路面的输入位移，zb为座椅的位移，zc为车身的位移，zd为轮胎的位移，kb、kc、kd分别为对应弹簧的阻尼系数，F1、F2为控制力；In the formula: m d is the unsprung mass, m c is the sprung mass, m b is the total mass of the person and the vehicle seat, z g is the input displacement of the road surface, z b is the displacement of the seat, z c is the displacement of the body, z d is the displacement of the tire, k b , k c , and k d are the damping coefficients of the corresponding springs respectively, and F1 and F2 are the control forces;
将上述微分方程写为状态空间方程可以得出：Writing the above differential equation as a state space equation gives:
得到状态矢量z(t),状态矩阵A，控制输入U(t)，控制输入矩阵B，噪声输入矩阵F，高斯白噪声W(t)；Get the state vector z(t), state matrix A, control input U(t), control input matrix B, noise input matrix F, and Gaussian white noise W(t);
U(t)＝[F1 F2]T，W(t)＝[wt]。 U(t)=[F 1 F 2 ] T , W(t)=[w t ].
本发明使用牛顿力学定律建立车-座椅主动悬架系统动力学模型微分方程，为了方便控制器的设计，将微分方程转换为状态空间方程。将状态空间方程离散化设计对应的模型预测控制器，设计控制步长和预测步长等关键参数，最终对车-座椅主动悬架系统的控制目标进行滚动优化，这里主要以降低车身垂向加速度作为主要的性能指标。The present invention uses Newton's laws of mechanics to establish the differential equation of the dynamic model of the vehicle-seat active suspension system. In order to facilitate the design of the controller, the differential equation is converted into a state space equation. Discretize the state space equation to design the corresponding model predictive controller, design key parameters such as control step size and prediction step size, and finally carry out rolling optimization of the control target of the vehicle-seat active suspension system, here mainly to reduce the vertical direction of the vehicle body Acceleration as the main performance indicator.
通过分析该模型可以得出在路面信息的输入下，主动悬架能够随着路面信息的不同实时调整主动悬架中CDC减振器的阻尼大小。假如车辆通过减速带或者坑洼路段时，汽车能够通过悬架在最短时间内恢复车辆的行驶状态，当汽车行驶在大长坡或者曲率较大的路段时，通过汽车底盘控制车轮的侧滑力等使得车辆的横纵向加速度峰值降低。因为造成乘员产生晕动症状的原因是车辆在行驶过程中，横向和纵向还有垂向加速度变化大，且加速度频率在一定的范围内。通过主动悬架的作用使得车辆更加稳定，能够有效降低晕车率。By analyzing this model, it can be concluded that under the input of road surface information, the active suspension can adjust the damping size of the CDC shock absorber in the active suspension in real time according to the different road surface information. If the vehicle passes through a speed bump or a potholed road section, the car can restore the vehicle's driving status in the shortest possible time through the suspension. When the car is driving on a long slope or a road section with a large curvature, the vehicle chassis can control the side slip force of the wheels. etc. to reduce the vehicle's peak lateral and longitudinal acceleration. The reason why passengers experience motion sickness is that when the vehicle is driving, the lateral, longitudinal and vertical acceleration changes greatly, and the acceleration frequency is within a certain range. The active suspension makes the vehicle more stable and can effectively reduce the motion sickness rate.
S2、在车辆前侧上方利用双目摄像头对路面信息进行提取，并使用双目视觉识别算法对车辆行驶过程中的随机路面信息进行识别，并且对路面信息进行等级划分，利用预瞄控制算法，将路面等级信息输入到车-座椅主动悬架系统模型中，作为激励信号输入；S2. Use a binocular camera above the front side of the vehicle to extract road information, use a binocular visual recognition algorithm to identify random road information while the vehicle is driving, classify the road information into grades, and use a preview control algorithm. Input road surface grade information into the vehicle-seat active suspension system model as an excitation signal input;
在双目摄像头对路面信息提取过程中，利用大量的含有不同路面信息的图片组建成数据库，基于VGGNet结构，整个网络都使用同样大小的卷积核和最大池化尺寸，利用VGG16神经网络对数据库中图片加入标签并进行训练学习；按照国际标准化不同路况下的时域扰动曲线不同，并以路面PSD的形式进行描述；通常采用下式拟合路面激励的功率谱：In the process of extracting road surface information from binocular cameras, a large number of pictures containing different road surface information are used to form a database. Based on the VGGNet structure, the entire network uses the same size convolution kernel and maximum pooling size. The VGG16 neural network is used to extract the database. Add labels to the pictures and conduct training and learning; according to international standardization, the time domain disturbance curves under different road conditions are different and described in the form of road PSD; the following formula is usually used to fit the power spectrum of road excitation:
其中：空间频率为n(m-1)；参考空间频率为n0；通常取n0＝0.1(m-1)。路面不平度系数为Gq(n0)(m3)，频率指数一般选为2。Among them: the spatial frequency is n(m -1 ); the reference spatial frequency is n 0 ; usually n 0 =0.1(m -1 ) is taken. The road surface roughness coefficient is G q (n 0 )(m 3 ), and the frequency index is generally selected as 2.
GB-7031-1986标准将路面不平度系数分为A～H共8个等级，其中A级路面为对应的高速公路及其路面状况，所以A级路面状况最佳，车辆通过性好，E级路面为未铺装路面，H级路面为最差路面状况，车辆通过性差。在对路面信息训练提取过程中，对不同标签下的路面图片聚类分析，根据各种路面的情况，一共按照国家等级标准分成8类路面，依次由路况最佳到路况最差。通过不同分类等级的路面信息，对照不同的路面激励信号，将路面激励信号输入到车-座椅主动悬架系统中，作为系统输入信息。The GB-7031-1986 standard divides the road roughness coefficient into 8 levels from A to H. The A-level road surface is the corresponding highway and its road surface condition, so the A-level road surface condition is the best and the vehicle passability is good, and the E-level road surface is the best. The road surface is unpaved, and the H-grade road is the worst road condition, with poor vehicle passability. During the training and extraction process of road surface information, the road surface images under different labels were clustered and analyzed. According to the conditions of various road surfaces, they were divided into 8 types of road surfaces according to national grade standards, from the best road conditions to the worst road conditions. Through road information of different classification levels, different road excitation signals are compared, and the road excitation signals are input into the vehicle-seat active suspension system as system input information.
通过图像识别技术，利用车上的双目摄像头对车前即将通过的地面进行提取数据，并且进行实时识别车辆通过路面的等级，将路面等级信息实时传递到车载控制器中，控制器发送指令信息，即时调整悬架系统的刚度，形成车辆悬架控制中的预瞄控制策略，有效提高车辆的平顺性和舒适性。控制器结合路面辨识结果、车速、距离等信息解算出系统的作动时刻，可以使系统在更加准确的时刻作动，升高和降低悬挂，增加或者减小阻尼，进而获得更好的舒适性和通过性。Through image recognition technology, the binocular camera on the vehicle is used to extract data from the ground that is about to pass in front of the vehicle, and the grade of the road surface that the vehicle is passing through is identified in real time, and the road grade information is transmitted to the vehicle-mounted controller in real time, and the controller sends instruction information. , instantly adjusts the stiffness of the suspension system to form a preview control strategy in vehicle suspension control, effectively improving the ride and comfort of the vehicle. The controller combines the road surface identification results, vehicle speed, distance and other information to calculate the system's actuation time, which can make the system act at a more accurate time, raise and lower the suspension, increase or decrease the damping, and thereby obtain better comfort. and passability.
利用图像识别算法对道路图片数据集中的各种类型图片进行训练学习，使用VGG-16算法来提高算法识别的准确率。在经过一系列前期的训练学习后，算法对车辆道路类型已经有了比较准确的识别度，通过使用车载双目摄像头对车辆行驶路面的不同类型进行识别，将不同等级的路面信息进行聚类分析，将所有路面均按相应的等级划分，将不同类型的路面等级输入到车载控制器端；在进行视觉识别过程中，假如识别到的路面等级为A级路面信息则将A级路面信息实时输入到车-座椅主动悬架系统中，形成悬架预瞄控制策略框架。自动驾驶车辆主动悬架ECU电控单元在收到路面激励信号后可以根据先验经验，控制车辆系统根据路面输入信息在线实时调整悬架阻尼刚度，从而更好地通过行驶的路面，提高自动驾驶车辆行驶的舒适性和平顺性。The image recognition algorithm is used to train and learn various types of pictures in the road picture data set, and the VGG-16 algorithm is used to improve the accuracy of algorithm recognition. After a series of preliminary training and learning, the algorithm has achieved a relatively accurate recognition of vehicle road types. It uses on-board binocular cameras to identify different types of vehicle road surfaces and performs cluster analysis on different levels of road surface information. , all roads are divided into corresponding grades, and different types of road grades are input to the on-board controller; during the visual recognition process, if the identified road grade is A-level road information, the A-level road information will be input in real time. Into the vehicle-seat active suspension system, a suspension preview control strategy framework is formed. After receiving the road excitation signal, the active suspension ECU electronic control unit of the autonomous driving vehicle can control the vehicle system to adjust the suspension damping stiffness online in real time based on the road input information based on prior experience, so as to better pass the driving road surface and improve automatic driving. The comfort and smoothness of vehicle driving.
S3、建立晕车模型系统感知车辆运动状态，包括车辆的运动激励信号，以及座椅悬架给乘员身体的激励信号；所述晕车模型系统主要包括乘员身体激励感知模块、车辆状态感知模块和用户乘车舒适性反馈模块；所述车辆状态感知模块用于感知车辆的运动状态，包括车辆的运动激励信号；所述乘员身体激励感知模块用于感知座椅悬架给乘员身体的激励信号；所述用户乘车舒适性反馈模块用于根据乘员的主观感受调节车辆运行的模式，及时切换车辆悬架系统运行模式。S3. Establish a motion sickness model system to sense the vehicle's motion state, including the vehicle's motion excitation signal and the seat suspension's excitation signal to the occupant's body; the motion sickness model system mainly includes the occupant's body excitation sensing module, the vehicle state sensing module and the user's ride. Vehicle comfort feedback module; the vehicle state sensing module is used to sense the motion state of the vehicle, including the vehicle's motion excitation signal; the passenger body excitation sensing module is used to sense the excitation signal from the seat suspension to the occupant's body; the The user ride comfort feedback module is used to adjust the vehicle operating mode according to the subjective feelings of the occupants and switch the vehicle suspension system operating mode in a timely manner.
由于乘员产生晕动症主要是由主观垂直冲突造成的，主观垂直冲突模型(SVC)主要考虑乘员被动运动的情况，忽略了人类大脑前庭功能以及视觉刺激等因素对晕车的影响。车辆在运行过程中假如车辆纵向和横向运动不稳定，在垂向运动中受路面输入激励信号的影响，均会使得乘员发生晕动。假如人体长时间处于振动的环境中，晕动症状也会逐渐加重，发生呕吐的几率也会增加。虽然人体对水平运动的敏感性略微高于垂直和俯仰方向，晕动病的贡献率主要源于水平方向的振动，但是在垂向振动对晕动病的发生也占有很大比重。在本发明中主要考虑由垂向激励信号的输入使得乘员因垂向激励频率在晕动症发生的范围内，如何降低乘客晕车症状的发生。这里主要以晕动病剂量值MSDV为晕动症发生的指标，对MSDV影响最大即权重最高的频率大致为0.16Hz。Since motion sickness among occupants is mainly caused by subjective vertical conflict, the Subjective Vertical Conflict Model (SVC) mainly considers the passive movement of the occupants and ignores the impact of factors such as the vestibular function of the human brain and visual stimulation on motion sickness. If the longitudinal and lateral movements of the vehicle are unstable during operation, and the vertical movement is affected by the input excitation signals from the road surface, the occupants will suffer from motion sickness. If the human body is exposed to a vibrating environment for a long time, the symptoms of motion sickness will gradually worsen, and the chance of vomiting will also increase. Although the human body is slightly more sensitive to horizontal motion than vertical and pitch directions, and the contribution rate of motion sickness mainly comes from vibration in the horizontal direction, vertical vibration also accounts for a large proportion of the occurrence of motion sickness. In the present invention, the main consideration is how to reduce the occurrence of motion sickness symptoms for passengers by making the vertical excitation frequency of the occupants within the range where motion sickness occurs due to the input of the vertical excitation signal. Here, the motion sickness dose value MSDV is mainly used as an indicator of the occurrence of motion sickness. The frequency that has the greatest impact on MSDV, that is, the highest weight, is roughly 0.16Hz.
其中：aw代表振动加权加速度，T不受时间限制。Among them: a w represents the vibration weighted acceleration, and T is not limited by time.
在车辆行驶过程中，路面激励信号通过车身不断传递给乘客，在车-椅主动悬架系统自适应调节阻尼特性的基础上，乘客可以根据个人的主观乘车感受，通过用户乘车舒适性反馈模块选择悬架系统运行模型，比如：悬架的软硬程度等。假如经过乘员身体激励感知模块计算，乘客晕动病剂量值大于一般水平，则乘客的乘车舒适性较差，则MSDV值较高。通过车身状态感知模块应该即时调整车辆驾驶模式，减缓乘员晕动症的发生。During the driving process of the vehicle, road excitation signals are continuously transmitted to the passengers through the body. Based on the adaptive adjustment of the damping characteristics of the vehicle-seat active suspension system, passengers can provide feedback on the user's riding comfort based on their personal subjective riding experience. The module selects the suspension system operation model, such as: the softness and hardness of the suspension, etc. If the passenger's motion sickness dose value is greater than the normal level calculated by the passenger body excitation sensing module, then the passenger's riding comfort will be poor and the MSDV value will be high. The vehicle's driving mode should be adjusted immediately through the body status sensing module to reduce the occurrence of motion sickness among occupants.
在自动驾驶车辆运行过程中，一般情况下乘员均坐于车内座椅上，乘员身体不断接受车轮传递上来的激励信号，最后经过人体大脑前庭系统等对激励信号进行感知处理。假如激励信号传递频率在人体最敏感范围内，可以导致人体不舒适甚至发生呕吐等现象，使得乘员的乘车体验感差。为了克服这一问题，乘员可以根据自己的主观感受即时切换车辆的驾驶模式，实时调整车-座椅主动悬架系统的阻尼特性。车辆中的各种感知模块也能通过一系列的传递信号，计算晕动发生剂量值是否超过人体正常承受值，假如超过人体正常承受值则通过车辆的状态感知模块反馈于车辆控制器中，车辆再进一步调整其运行状态，降低晕动剂量值(MSDV)。During the operation of an autonomous vehicle, the occupants generally sit on the seats in the vehicle. The occupant's body continuously receives the excitation signals transmitted from the wheels, and finally the excitation signals are sensed and processed by the vestibular system of the human brain. If the frequency of excitation signal transmission is within the most sensitive range of the human body, it can cause discomfort or even vomiting, making the passenger's ride experience poor. In order to overcome this problem, passengers can instantly switch the vehicle's driving mode according to their own subjective feelings and adjust the damping characteristics of the car-seat active suspension system in real time. Various sensing modules in the vehicle can also calculate whether the motion sickness dose value exceeds the normal tolerance value of the human body through a series of transmission signals. If it exceeds the normal tolerance value of the human body, it will be fed back to the vehicle controller through the vehicle's status sensing module. Then further adjust its operating status to reduce the motion sickness dose value (MSDV).
S4、使用模型预测控制(MPC)算法对主动悬架性能指标进行滚动优化，降低主动悬架系统的垂向加速度值，充分考虑车辆悬架的垂向加速度产生车辆垂向振动进而造成乘员晕动症发生的影响，将关键性能指标分别进行控制，通过改进算法来改善车辆的垂向加速度大小，最终有效改善自动驾驶车辆的平顺性和操纵稳定性；具体包括：S4. Use the model predictive control (MPC) algorithm to perform rolling optimization of active suspension performance indicators, reduce the vertical acceleration value of the active suspension system, and fully consider the vertical acceleration of the vehicle suspension to produce vertical vibration of the vehicle and thereby cause motion sickness of the occupants. To control the impact of symptoms, key performance indicators are controlled respectively, and the vehicle's vertical acceleration is improved through improved algorithms, ultimately effectively improving the ride comfort and handling stability of autonomous vehicles; specifically including:
对步骤S1推导得出的车-座椅主动悬架系统动力学模型进行MPC算法设计，通过MPC算法采用离散模型预测被控对象的未来状态，并且通过求解有限时域内的最优化问题得到最优控制量，将连续的车辆主动悬架动力学方程离散化：The MPC algorithm is designed for the vehicle-seat active suspension system dynamics model derived in step S1. The MPC algorithm uses a discrete model to predict the future state of the controlled object, and the optimal solution is obtained by solving the optimization problem in the limited time domain. The control quantity discretizes the continuous vehicle active suspension dynamics equation:
式中：Ad＝eAT,T为控制步长；x(k|k)，ω(k|k)为第K时刻的测量量，代表在k时刻的真实系统状态和路面激励；y(k|k)，u(k|k)和x(k+1|k)为k时刻的预测量，代表在k时刻预测的k时刻系统输出，k时刻控制量和k+1时刻系统状态；In the formula: A d = e AT , T is the control step size; x(k|k), ω(k|k) are the measurement quantities at the Kth moment, representing the real system state and road excitation at the k moment; y(k|k), u(k| k) and x(k+1|k) are the predicted quantities at moment k, representing the system output at moment k predicted at moment k, the control quantity at moment k and the system state at moment k+1;
为了保证车辆具有良好的平顺性和操纵稳定性，设定MPC控制器优化问题的目标函数为minJ(y,u)，即使得系统的输出和阻尼力尽可能小，减小路面激励给人体带来的冲击；In order to ensure that the vehicle has good ride comfort and handling stability, the objective function of the MPC controller optimization problem is set to minJ(y,u), that is, to make the output and damping force of the system as small as possible and reduce the impact of road excitation on the human body. the coming shock;
式中：Q和R分别为权重矩阵。In the formula: Q and R are weight matrices respectively.
本发明未详尽事宜皆为公知技术。Matters not detailed in the present invention are all well-known technologies.
以上所述的具体实施方式仅仅是对本发明的优选实施方式进行描述，并非对本发明的范围进行限定，在不脱离本发明设计精神的前提下，本领域普通技术人员对本发明的技术方案做出的各种变形和改进，均应落入本发明权利要求书确定的保护范围内。The above-described specific embodiments are only descriptions of preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, those of ordinary skill in the art can make technical solutions to the present invention. Various modifications and improvements should fall within the protection scope determined by the claims of the present invention.
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| CN117554097A (en) * | 2024-01-12 | 2024-02-13 | 山东鲁岳桥机械股份有限公司 | Intelligent monitoring device for vehicle suspension faults |
| CN117786955A (en) * | 2023-12-05 | 2024-03-29 | 中铁大桥局集团有限公司 | Method for evaluating susceptibility of vortex-induced vibration span of large-span bridge |
| CN118894118A (en) * | 2024-08-05 | 2024-11-05 | 南京理工大学 | A method and system for evaluating occupant motion sickness during driving of an intelligent chassis |
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| CN117786955A (en) * | 2023-12-05 | 2024-03-29 | 中铁大桥局集团有限公司 | Method for evaluating susceptibility of vortex-induced vibration span of large-span bridge |
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