Analysis of Automatic Steering Control for Highway Vehicles with Look-down Lateral Reference Systems

SUMMARY

In this paper, steering control for passenger cars on automated highways is analyzed, concentrating on look-down reference systems. Extension of earlier experimental results for low speed to highway speed is shown to be non-trivial. The limitations of pure output-feedback of lateral vehicle displacement from the road reference are examined under practical constraints and performance requirements like robustness, maximum lateral error and comfort. The in-depth system analysis directly leads to a new alternative design direction which allows to preserve look-ahead reference systems for highway speed automatic driving.     

基于hp自适应伪谱法的智能汽车紧急变道轨迹规划与优化

为了提高智能汽车紧急变道轨迹规划的实时性和适应性，将紧急变道过程分为初始阶段和跟踪阶段，初始阶段的轨迹由优秀驾驶人紧急变道模型产生，跟踪阶段的轨迹采用Sigmoid函数规划出紧急避让路径。首先通过聚类分析处理优秀驾驶人转向操作的实车试验数据，拟合得出紧急变道过程中的方向盘转角随时间的关系（即驾驶人紧急变道模型），作为智能汽车在紧急变道初始阶段不同速度下车辆控制的输入量。然后通过建立与求解约束方程，满足避撞约束、侧向位移约束以及最大侧向加速度约束，得出Sigmoid函数表达式，作为智能汽车在紧急变道过程跟踪阶段的参考路径。最后利用hp自适应伪谱法加入切换点的物理量约束，逼近全局正交多项式的状态量和控制量，自动调整和处理2个阶段的切换点位置和衔接问题，以最小变道距离为目标对跟踪阶段的变道轨迹进行优化。运用PreScan与MATLAB对4种不同工况下的紧急变道轨迹规划进行联合仿真。结果表明：提出的轨迹规划与优化方法在满足各项约束的情况下成功避开障碍物，同时缩短了需要优化的轨迹，优化时间都小于0.9 s，并且与基于多项式函数轨迹规划方法相比，该方法能够以距障碍物较远的距离避开障碍物，在不同的车辆速度、道路曲率和障碍物宽度的复杂工况下具有更好的适应性。     

智能汽车自动紧急转向避撞的跟踪控制方法对比研究

为了提高智能汽车的主动安全性,提出3种不同的自动紧急转向避撞跟踪控制方法。首先建立汽车避撞简化模型,对制动、转向及两者相结合的3种不同避撞方式进行对比分析。其次,为深入研究汽车避撞过程中的实际响应,建立包含转向、制动及悬架3个子系统耦合特性的底盘18自由度统一动力学模型,并进行相关试验验证。随后构建智能汽车自动紧急转向避撞控制框架,对五次多项式参考路径和七次多项式参考路径的横摆角速度和横摆角加速度进行对比分析。接着以线性2自由度转向动力学模型为参考对象,对最优控制四轮转向、最优控制前轮转向、前馈与反馈控制相结合的前轮转向3种不同的跟踪控制系统分别进行设计。最后,以汽车底盘18自由度统一动力学模型为研究对象,对上述3种避撞控制系统进行仿真试验对比分析。研究结果表明：与制动避撞相比而言,转向避撞所需的纵向距离有较大降低,随着车速的增加和路面附着系数的越低,效果越明显;七次多项式参考路径比五次多项式参考路径的避撞过渡过程更为平缓,当实际车速与控制器所用车速不一致时,前者避撞性能表现更优;最优四轮转向控制系统在高、低2种不同附着路面都具有较好的避撞效果,最优前轮转向控制系统次之,而前馈与反馈相结合的前轮转向控制系统在低附着路面上则表现出严重的失稳。     

Robust hierarchical controller with conditional integrator based on small gain theorem for reference trajectory tracking of autonomous vehicles

ABSTRACT

A robust trajectory tracking controller is designed for autonomous vehicles based on a hierarchical architecture to make the autonomous vehicle track a given reference trajectory. The controller consists of two sub controllers: kinematic controller and dynamic controller. Based on the kinematics of tracking reference trajectory, a desired yaw rate is calculated by kinematic controller to make the lateral deviation global asymptotic stable. Then, steering wheel angle is calculated by a vehicle dynamic controller to make the vehicle yaw rate converge to the desired value and make the vehicle dynamic stable. Conditional integration method is used in the sub controllers. This method guarantees global asymptotic stability of tracking reference values and considers the uncertainty of parameters and constraints of desired yaw rate and actuators. Then based on small-gain theorem, the condition of the finite-gain L stability is given to the hierarchical controller to ensure the interconnected sub systems stable and prevent the amplification of system disturbance. Finally, the effectiveness and robustness of the controller are validated by real vehicle experiments.     

Trends in vehicle motion control for automated driving on public roads

ABSTRACT

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed.     

Lane-following method for high speed autonomous vehicles

This paper presents a steering control method for lane-following in a vehicle using an image sensor. With each image frame acquired from the sensor, the steering control method determines target position and direction, and constructs a travel path from the current position to the target position either as an Arc-path or S-path. The steering angle is calculated from the travel path thus generated, and the vehicle follows the travel path via motor-control. The method was tested using a vehicle dubbed as KAV (Korea Autonomous Vehicle) along an expressway (Seoul Inner Beltway) trajectory with a variety of radii (50 m ∼ 300 m) while traveling at a speed of 60 km/h to 80 km/h. Compared with an experienced human driver, the method showed little much difference in performance in terms of lane-center deviation. The proposed method is currently employed for high speed autonomous driving as well as for stop and go traffic.     

制动与转向协调动作的车辆避撞控制研究（双语出版）

为了弥补现有汽车避撞控制策略以及碰撞风险评价指标单一的不足，提出转向和制动协调的主动避撞控制系统。首先规划了五次多项式换道路径，在对其理论分析的基础上得到转向临界避撞距离和与目标车道车辆的安全距离约束。其次，考虑道路附着系数和系统延迟的影响，基于制动过程给出制动临界避撞距离，并以纵向行驶安全系数ξ和碰撞时间倒数T^-1_TC划分安全行驶区域，利用驾驶人实车跟车数据标定稳态跟随/定速巡航区域的阈值。随后，通过转向/制动临界避撞距离的对比给出2种避撞方式的安全收益范围。最后搭建Simulink/CarSim联合仿真模型，并对其进行不同初始条件下的避撞仿真试验。研究结果表明：转向操作在制动距离不足时仍是有效的；当主车高速近距离接近静止前车时，主车可以顺利采取转向换道动作，而常规ACC系统在2.5 s处的车间相对距离为-0.76 m，事实上已经发生了碰撞；当相邻车道前车与主车纵向间距不满足换道安全距离约束时，避撞控制系统进入紧急制动模式，最大制动减速度达到-0.8g（g为重力加速度），实际最小车间距为5.1 m；通过转向和制动的协调动作，充分发挥了车辆的避撞潜力；ξ和T^-1_TC指标的融合，可以更好地评估碰撞风险并实现不同控制模式的转换，在保证行车安全的同时可避免过分制动给乘客造成的紧张感。     

无人驾驶汽车路径跟踪控制方法拟人程度研究

无人驾驶汽车路径跟踪控制是无人驾驶汽车运动控制的核心所在,目前常用的路径跟踪模型主要以路径跟踪精度为主要控制目标,在很大程度上忽略了无人驾驶汽车的乘坐舒适性和控制的拟人程度。为了研究无人驾驶汽车路径跟踪控制算法的拟人程度并提高乘坐舒适性,基于转向几何学、汽车运动学和汽车动力学理论建立实车中常用的4种路径跟踪模型,提出以路径跟踪过程中的最大横向加速度a_ymax和方向盘转角平方和δ_w²共同表征路径跟踪模型的拟人程度和横向乘坐舒适性。基于驾驶人实车换道试验数据,建立多项式拟人换道参考路径,搭建CarSim/Simulink联合仿真模型,并对其进行不同车速下的车辆换道试验。研究结果表明：路径跟踪模型的横向循迹偏差均会随着车速的提高而增加,但都能较好实现路径跟踪;带预瞄路径跟踪模型和动力学前馈最优LQR路径跟踪模型拟人程度较好;汽车运动学路径跟踪模型的乘坐舒适性最差,方向盘修正激烈;在100 km·h^-1,a_ymax>0.7 m·s^-2,δ_w²>2.7×10³时,拟人程度最差;不带预瞄路径跟踪模型循迹精度最高,且拟人程度最高,乘坐舒适性最好,120 km·h^-1时,a_ymax ≤ 0.5 m·s^-2。     

Application of time-variant predictive control to modelling driver steering skill

The paper addresses the need for improved mathematical models of human steering control. A multiple-model structure for a driver's internal model of a nonlinear vehicle is proposed. The multiple-model structure potentially offers a straightforward way to represent a range of driver expertise. The internal model is combined with a model predictive steering controller. The controller generates a steering command through the minimisation of a cost function involving vehicle path error. A study of the controller performance during an aggressive, nonlinear steering manoeuvre is provided. Analysis of the controller performance reveals a reduction in the closed-loop controller bandwidth with increasing tyre saturation and fixed controller gains. A parameter study demonstrates that increasing the multiple-model density, increasing the weights on the path error, and increasing the controller knowledge range all improved the path following accuracy of the controller.     

A novel integrated chassis controller for full drive-by-wire vehicles

In this paper, a systematic design with multiple hierarchical layers is adopted in the integrated chassis controller for full drive-by-wire vehicles. A reference model and the optimal preview acceleration driver model are utilised in the driver control layer to describe and realise the driver's anticipation of the vehicle's handling characteristics, respectively. Both the sliding mode control and terminal sliding mode control techniques are employed in the vehicle motion control (MC) layer to determine the MC efforts such that better tracking performance can be attained. In the tyre force allocation layer, a polygonal simplification method is proposed to deal with the constraints of the tyre adhesive limits efficiently and effectively, whereby the load transfer due to both roll and pitch is also taken into account which directly affects the constraints. By calculating the motor torque and steering angle of each wheel in the executive layer, the total workload of four wheels is minimised during normal driving, whereas the MC efforts are maximised in extreme handling conditions. The proposed controller is validated through simulation to improve vehicle stability and handling performance in both open- and closed-loop manoeuvres.     

4WS汽车操纵稳定性建模和仿真研究

从改善汽车操纵稳定性角度出发,全面考虑轮胎载荷、转向系等对四轮转向汽车操纵稳定性的影响,建立4WS（4 Wheel Steering System）整车操纵稳定模型。并用Matalab/simulink进行了模拟仿真研究。比较性地研究汽车在低速和高速时控制方式的特点和不同之处。本模型为4WS汽车设计改进优化提供一种手段和方法,同时,为4WS汽车理论研究和试验校核提供了参考。     

Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

The aim of this work is to develop a comprehensive yet practical driver model to be used in studying driver–vehicle interactions. Drivers interact with their vehicle and the road through the steering wheel. This interaction forms a closed-loop coupled human–machine system, which influences the driver's steering feel and control performance. A hierarchical approach is proposed here to capture the complexity of the driver's neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities, especially in the presence of external disturbance. The proposed motor control framework has three layers: the first (or the path planning) plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory; the second (or the musculoskeletal controller) actuates the musculoskeletal arm to rotate the steering wheel accordingly; and the final layer ensures the precision control and disturbance rejection of the motor control units. The physics-based driver model presented here can also provide insights into vehicle control in relaxed and tensed driving conditions, which are simulated by adjusting the driver model parameters such as cognition delay and muscle co-contraction dynamics.     

面向车道变换的路径规划及模型预测轨迹跟踪

为解决智能车辆在车道变换过程中的路径规划和路径跟踪问题,首先,利用梯形加速度法设计了车道变换虚拟理想轨迹,该路径规划方法的适应性取决于车道变换时间、横向加速度及变化率等关键变量的约束条件,因而对各关键变量之间的数学关系进行了定量计算,并绘制了不同工况下的车道变换虚拟理想轨迹,用于分析各关键变量对路径规划的影响;其次,建立了线性离散的车辆动力学预测模型,综合分析了车辆模型的控制输入、状态变量以及道路结构参数等约束条件,构建了多约束模型预测控制（MMPC）系统用于车道变换路径跟踪,并基于Hildreth二次规划算法对其目标函数进行了求解,获得前轮转向角控制量,从而保证智能车辆在车道变换过程中的路径跟踪性能及操纵稳定性能;最后,利用MATLAB和Carsim软件对提出的多约束模型预测控制系统进行联合仿真,并构建单约束模型预测控制（SMPC）系统与其进行性能比较,分别对车道变换时间为3 s和6 s时的车道变换性能进行比较分析。结果表明：当车道变换时间为6 s时,2种控制系统都能较好地实现车道变换功能;当车道变换时间为3 s时,与SMPC控制系统相比较,MMPC控制系统能够在有效跟踪期望行驶路径的同时改善车辆的操纵稳定性,从而提高车辆在路径跟踪过程中的主动安全性能。     

Real-time characterisation of driver steering behaviour

In recent years the application of driver steering models has extended from the off-line simulation environment to autonomous vehicles research and the support of driver assistance systems. For these new environments there is a need for the model to be adaptive in real time, so the supporting vehicle systems can react to changes in the driver, their driving style, mood and skill. This paper provides a novel means to meet these needs by combining a simple driver model with a single-track vehicle handling model in a parameter estimating filter – in this case, an unscented Kalman filter. Although the steering model is simple, a motion simulator study shows it is capable of characterising a range of driving styles and may also indicate the level of skill of the driver. The resulting filter is also efficient – comfortably operating faster than real time – and it requires only steer and speed measurements from the vehicle in addition to the reference path. Adaptation of the steer model parameters is demonstrated along with robustness of the filter to errors in initial conditions, using data from five test drivers in vehicle tests carried out on the open road.

Abbreviations: ADAS: advanced driver assistance systems; CG: centre of gravity; CAN: controller area network; EKF: extended Kalman filter; GPS: global positioning system; UKF: unscented Kalman filter     

Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

ABSTRACT

Collision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require large actuator inputs, together with highly nonlinear tyre cornering response. In order to avoid collision while stabilising autonomous vehicle under dynamic driving situations at handling limits, this paper proposes a novel emergency steering control strategy based on hierarchical control architecture consisting of decision-making layer and motion control layer. In decision-making layer, a dynamic threat assessment model continuously evaluates the risk associated with collision and destabilisation, and a path planner based on kinematics and dynamics of vehicle system determines a collision-free path when it suddenly encounters emergency scenarios. In motion control layer, a lateral motion controller considering nonlinearity of tyre cornering response and unknown external disturbance is designed using tyre lateral force estimation-based backstepping sliding-mode control to track a collision-free path, and to ensure the robustness and stability of the closed-loop system. Both simulation and experiment results show that the proposed control scheme can effectively perform an emergency collision avoidance manoeuvre while maintaining the stability of autonomous vehicle in different running conditions.     

Additional 4WS and Driver Interaction

SUMMARY

This investigation is based on a complex 4-wheel vehicle model of a passenger car that includes steering system and drive train. The tyre properties are described for all possible combined longitudinal and lateral slip values and for arbitrary friction conditions. The active part is an additional steering system of all 4 wheels, additionally to the driver's steering wheel angle input. Three control levels are used for the driver model that thereby can follow a given trajectory or avoid an obstacle.

The feedback control of the additional 4 wheel steering is based on an observer which can also have adaptive characteristics. Moreover a virtual vehicle model in a feedforward scheme can provide desired steering characteristics.

To get information for critical situations a cornering manoeuvre with sudden u-split conditions is simulated. Further a similar manoeuvre is used to evaluate the reentry in a high friction area from low friction conditions. And finally the performance of the controller is shown in a severe lane change manoeuvre.     

装载机避障轨迹规划及模型预测轨迹跟踪

轮式装载机在工作区域行驶时，避障过程频繁，以往的避障轨迹规划未考虑整车转向半径约束和车速变化，也较少考虑整车在动力学模型条件下的轨迹跟踪性能。针对上述情况，以自动驾驶轮式装载机为对象，基于最优快速随机扩展树算法（RRT^*），考虑车身膨胀圆个数，生成全局最优避障路径，以整车最小稳定转向半径为约束，利用CC-Steer算法对避障路径进行平滑处理，采用路径-速度分解算法规划满足整车在加速、匀速和减速状态下的避障行驶轨迹。基于整车动力学模型，考虑行驶过程中的横向位置偏差和航向角偏差，并将整车动力传动系统视为1阶惯性环节，构建装载机动力学状态空间方程。以加速度和铰接角为控制输入，以车速、横向位置偏差和航向角偏差为控制输出，建立整车动力学预测模型，以加速度、铰接角和车速为约束条件，将目标函数转换为二次规划问题，建立满足装载机在工作区域避障的模型预测轨迹跟踪控制系统。以规划的非匀速行驶避障轨迹为目标，利用构建的模型预测轨迹跟踪系统，进行自动驾驶轮式装载机的轨迹跟踪仿真。研究结果表明：所提方法能够很好地控制自动驾驶轮式装载机从初始位姿驶向目标位姿，实现整车在工作区域的避障过程，且在避障过程中满足整车的约束要求，保证整车在轨迹跟踪过程中的安全稳定性能。     

Unified decision-making and control for highway collision avoidance using active front steer and individual wheel torque control

ABSTRACT

Collision avoidance is a crucial function for all ground vehicles, and using integrated chassis systems to support the driver presents a growing opportunity in active safety. With actuators such as in-wheel electric motors, active front steer and individual wheel brake control, there is an opportunity to develop integrated chassis systems that fully support the driver in safety critical situations. Here we consider the scenario of an impending frontal collision with a stationary or slower moving vehicle in the same driving lane. Traditionally, researchers have approached the required collision avoidance manoeuver as a hierarchical scheme, which separates the decision-making, path planning and path tracking. In this context, a key decision is whether to perform straight-line braking, or steer to change lanes, or indeed perform combined braking and steering. This paper approaches the collision avoidance directly from the perspective of constrained dynamic optimisation, using a single optimisation procedure to cover these aspects within a single online optimisation scheme of model predictive control (MPC). While the new approach is demonstrated in the context of a fully autonomous safety system, it is expected that the same approach can incorporate driver inputs as additional constraints, yielding a flexible and coherent driver assistance system.     

新型视觉区域智能车辆导航控制器设计

简要介绍了基于机器视觉导航区域智能车辆(CyberCar)的导航原理和组成。首先采用逆M序列作为辨识输入信号和最小二乘算法得到车辆转向系统的系统辨识特征方程,结合预瞄运动学模型和车辆二自由度转向动力学模型,从而建立车辆基于视觉预瞄的转向动力学控制数学模型,根据线性二次型最优控制理论得到状态线性反馈的最优控制规律。通过仿真分析和试验,验证了最优控制器在CyberCar户外路径跟踪过程中平稳、可靠。