Optimal motion control for collision avoidance at Left Turn Across Path/Opposite Direction intersection scenarios using electric propulsion

Collision avoidance at intersections involving a host vehicle turning left across the path of an oncoming vehicle (Left Turn Across Path/Opposite Direction) have been studied in the past, but mostly using simplified interventions and rarely considering the possibility of crossing the intersection ahead of a bullet vehicle. Such a scenario where the driver preference is to avoid a collision by crossing the intersection ahead of a bullet vehicle is considered in this work. The optimal vehicle motion for collision avoidance in this scenario is determined analytically using a particle model within an optimal control framework. The optimal manoeuvres are then verified through numerical optimisations using a two-track vehicle model, where it was seen that the wheel forces followed the analytical global force angle result independently of the other wheels. A Modified Hamiltonian Algorithm controller for collision avoidance that uses the analytical optimal control solution is then implemented and tested in CarMaker simulations using a validated Volvo XC90 vehicle model. Simulation results showed that collision risk can be significantly reduced in this scenario using the proposed controller, and that more benefit can be expected in scenarios that require larger speed changes.     

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。     

基于微分几何的四轮独立驱动车辆运动解耦控制研究

针对车辆在纵向运动和横摆运动时的强耦合关系给车辆动力学控制带来的困难，以四轮独立电驱动车辆作为研究对象，基于微分几何理论设计了车辆系统运动解耦控制方法，将非线性强耦合的四轮驱动车辆动力学系统解耦为纵向和横向两个相对独立运动控制子系统，并设计了鲁棒控制器，以提高抵抗车辆行驶时不确定外力如侧风的干扰能力。基于 Trucksim 软件建立四轮驱动车辆模型，并针对车辆解耦控制策略和抗干扰策略进行了仿真测试。结果表明，相比于无解耦控制的车辆，采用微分几何解耦控制的四轮独立驱动车辆纵向速度偏差降低了 82.1%，横摆角速度偏差降低了80.7%，且微风干扰下的抗干扰能力明显改善，车辆稳定性显著提升。为验证该运动解耦控制策略在实时系统中的控制效果，还进行了硬件在环试验，结果表明，硬件在环试验的结果与仿真结果一致。     

Full drive-by-wire dynamic control for four-wheel-steer all-wheel-drive vehicles

Most of the controllers introduced for four-wheel-steer (4WS) vehicles are derived with the assumption that the longitudinal speed of the vehicle is constant. However, in real applications, the longitudinal speed varies, and the longitudinal, lateral, and yaw dynamics are coupled. In this paper, the longitudinal dynamics of the vehicle as well as its lateral and yaw motions are controlled simultaneously. This way, the effect of driving/braking forces of the tires on the lateral and yaw motions of the vehicle are automatically included in the control laws. To address the dynamic parameter uncertainty of the vehicle, a chatter-free variable structure controller is introduced. Elimination of chatter is achieved by introducing a dynamically adaptive boundary layer thickness. It is shown via simulations that the proposed control approach performs more robustly than the controllers developed based on dynamic models, in which longitudinal speed is assumed to be constant, and only lateral speed and yaw rate are used as system states. Furthermore, this approach supports all-wheel-drive vehicles. Front-wheel-drive or rear-wheel-drive vehicles are also supported as special cases of an all-wheel-drive vehicle.     

智能网联车辆交通流优化对交通安全的改善

为改善常规驾驶车辆交通流追尾碰撞交通安全状况,提出智能网联车辆（Connected and Automated Vehicles,CAV）与常规车辆构成的混合交通流车队稳定性优化控制方法。基于全速度差模型,应用集成速度与加速度的多前车反馈构建CAV跟驰模型,考虑CAV混合交通流车辆空间分布的随机性,将各类型局部车队稳定性作为优化目标,以局部车队头车速度扰动为系统输入,以尾车速度扰动为系统输出,应用经典控制理论领域的传递函数法推导局部车队稳定性约束条件;分析关于平衡态速度与CAV反馈系数的车队稳定域,以各类型局部车队能够在任意平衡态速度下均稳定为控制目标,对CAV反馈系数输出进行优化控制;设计高速公路上匝道交通瓶颈数值仿真试验,在不同CAV比例等多种条件下,分析CAV混合交通流优化控制对交通流车辆追尾碰撞风险的影响。研究结果表明：CAV混合交通流优化控制可降低车辆追尾碰撞风险,在碰撞时间阈值小于2 s时,100%比例的CAV交通流可将交通流的车辆追尾碰撞风险降低85.81%以上;在碰撞时间阈值大于2 s时,追尾碰撞风险可降低48.22%～78.80%。所提优化控制方法可有效降低CAV车队优化控制的复杂性,为大规模CAV背景下的混合交通流优化控制以及车辆追尾碰撞交通安全提升策略提供直接理论参考。     

Design and Evaluation of Robust Cooperative Adaptive Cruise Control Systems in Parameter Space

This paper is on the design of cooperative adaptive cruise control systems for automated driving of platoons of vehicles in the longitudinal direction. Longitudinal models of vehicles with simple dynamics, an uncertain first order time constant and vehicle to vehicle communication with a communication delay are used in the vehicle modeling. A robust parameter space approach is developed and applied to the design of the cooperative adaptive cruise control system. D-stability is chosen as the robust performance goal and the feedback PD controller is designed in controller parameter space to achieve this D-stability goal for a range of possible longitudinal dynamics time constants and different values of time gap. Preceding vehicle acceleration is sent to the ego vehicle using vehicle to vehicle communication and a feedforward controller is used in this inter-vehicle loop to improve performance. Simulation results of an eight vehicle platoon of heterogeneous vehicles are presented and evaluated to demonstrate the efficiency of the proposed design method. Also, the proposed method is compared with a benchmark controller and the feedback only controller. Time gap regulation and string stability are used to assess performance and the effect of the vehicle to vehicle communication frequency on control system performance is also investigated.     

基于扰动观测的智能驾驶主动抗扰纵向车速控制算法

为了减少智能驾驶车辆的纵向车速控制的时滞,提高主动抗扰性,提出一种基于扰动观测的纵向车速控制算法,并进行了实车验证。模型中,采用前馈控制模块,并提前输出控制量,来提高车速跟随的响应性;以主动抗扰控制(ADRC)模块作为反馈环节,采用扩张状态观测器(ESO)在线估计内外部扰动,并在控制端进行补偿,实现了对车速的精确闭环控制。在弯道、环岛等路况下进行了实车实验。结果表明:该算法可以在5 s内控制车速从怠速快速跟踪到目标车速,总体平均误差为0.17 km/h。因而,该算法较传统的比例积分微分(PID)有更好的响应性、控制精度和抗扰性。     

Lateral handling improvement with dynamic curvature control for an independent rear wheel drive EV

The integrated longitudinal and lateral dynamic motion control is important for four wheel independent drive (4WID) electric vehicles. Under critical driving conditions, direct yaw moment control (DYC) has been proved as effective for vehicle handling stability and maneuverability by implementing optimized torque distribution of each wheel, especially with independent wheel drive electric vehicles. The intended vehicle path upon driver steering input is heavily depending on the instantaneous vehicle speed, body side slip and yaw rate of a vehicle, which can directly affect the steering effort of driver. In this paper, we propose a dynamic curvature controller (DCC) by applying a the dynamic curvature of the path, derived from vehicle dynamic state variables; yaw rate, side slip angle, and speed of a vehicle. The proposed controller, combined with DYC and wheel longitudinal slip control, is to utilize the dynamic curvature as a target control parameter for a feedback, avoiding estimating the vehicle side-slip angle. The effectiveness of the proposed controller, in view of stability and improved handling, has been validated with numerical simulations and a series of experiments during cornering engaging a disturbance torque driven by two rear independent in-wheel motors of a 4WD micro electric vehicle.     

Automated Vehicle Merging Maneuver Implementation for AHS

Summary This paper presents a real-time implementation of a general merging algorithm for automated highway systems. A merging control problem is proposed first. A real-time algorithm is then presented, which is used to calculate a smooth reference speed trajectory for the merging vehicle based on the speed of the main lane vehicle. This algorithm can also be applied even when the main lane vehicles change speed. To make the algorithm adapt to different road layouts and to increase safety, a concept of virtual platooning is proposed. It effectively shifts the time of platoon formation forward prior to the start of real merging. Aspects closely related to real-time implementation are discussed, such as the controller adopted, the use of magnetometer based distance measurement and information passing by communication from main lane vehicles. Test results are presented and briefly analyzed.     

Connected variable speed limits control and car-following control with vehicle-infrastructure communication to resolve stop-and-go waves

The vision of intelligent vehicles traveling in road networks has prompted numerous concepts to control future traffic flow, one of which is the in-vehicle actuation of traffic control commands. The key of this concept is using intelligent vehicles as actuators for traffic control systems. Under this concept, we design and test a control system that connects a traffic controller with in-vehicle controllers via vehicle-to-infrastructure communication. The link-level traffic controller regulates traffic speeds through variable speed limits (VSL) gantries to resolve stop-and-go waves, while intelligent vehicles control accelerations through vehicle propulsion and brake systems to optimize their local situations. It is assumed that each intelligent vehicle receives VSL commands from the traffic controller and uses them as variable parameters for the local vehicle controller. Feasibility and effectiveness of the connected control paradigm are tested with simulation on a two-lane freeway stretch with intelligent vehicles randomly distributed among human-driven vehicles. Simulation shows that the connected VSL and vehicle control system improves traffic efficiency and sustainability; that is, total time spent in the network and average fuel consumption rate are reduced compared to (uncontrolled and controlled) scenarios with 100% human drivers and to uncontrolled scenarios with the same intelligent vehicle penetration rates.     

车-车协同下无人驾驶车辆的换道汇入控制方法

多车协同驾驶是智能车路系统领域的研究热点之一,可有效降低道路交通控制管理的复杂程度,减少环境污染的同时保障道路交通安全。基于多车协同驾驶控制结构,提出了一种无人驾驶车辆换道汇入的驾驶模型及策略,系统分析了多车协同运行状态的稳定条件。在综合分析无人驾驶车辆换道汇入的协作准则、安全性评估后,基于高阶多项式方法,结合车辆运行特性,通过引入乘坐舒适性的指标函数,设计得到无人驾驶车辆换道汇入的有效运动轨迹。通过研究汇入车辆与车队中汇入点前、后各车辆的运动关系,详细分析车辆发生碰撞的类型和影响因素,给出避免碰撞的条件准则,从而确保无人驾驶车辆汇入过程中多车行驶的安全性和稳定性。基于车辆运动学建立车辆位置误差模型,结合系统大范围渐进稳定的条件,选取线速度和角速度作为输入,应用李雅普诺夫稳定性理论和Backstepping非线性控制算法,设计了无人驾驶车辆换道汇入后的路径跟踪控制器。仿真试验和实车试验结果表明：所设计的换道汇入路径是可行、安全的,控制器具有良好的跟踪效果,纵向和横向的距离误差在15 cm以内,方向偏差的相对误差在10%以内。研究结果为智能车路系统中的多车状态变迁与协同驾驶研究提供了参考,可服务于未来道路交通安全设计和评价。     

矿用无人运输车辆轨迹跟踪控制算法研究

矿用无人运输车辆作业环境恶劣,存在大曲率弯道、坡道等非结构化道路明显特征,对无人化运输控制要求高。为改善PID等传统控制算法适应性问题,提高无人驾驶轨迹跟踪的车辆横纵向控制精度,提出一种纯跟踪与PID结合的多点预瞄横向控制、考虑模糊控制表参数拟合的纵向控制方法,减少控制参数的同时提高算法效果。根据传统控制算法设计基础控制器,结合基础算法优势进行横向与纵向控制算法设计,通过硬件在环仿真和实车测试验证算法的性能。试验结果表明,横向控制算法与斯坦利算法相比,车辆路径跟踪精度有明显改善,纵向控制方面,速度跟随误差<1 km/h,保证了车辆驾驶时的平稳性与舒适性。     

Design and hardware-in-loop implementation of collision avoidance algorithms for heavy commercial road vehicles

An important aspect from the perspective of operational safety of heavy road vehicles is the detection and avoidance of collisions, particularly at high speeds. The development of a collision avoidance system is the overall focus of the research presented in this paper. The collision avoidance algorithm was developed using a sliding mode controller (SMC) and compared to one developed using linear full state feedback in terms of performance and controller effort. Important dynamic characteristics such as load transfer during braking, tyre-road interaction, dynamic brake force distribution and pneumatic brake system response were considered. The effect of aerodynamic drag on the controller performance was also studied. The developed control algorithms have been implemented on a Hardware-in-Loop experimental set-up equipped with the vehicle dynamic simulation software, IPG/TruckMaker®. The evaluation has been performed for realistic traffic scenarios with different loading and road conditions. The Hardware-in-Loop experimental results showed that the SMC and full state feedback controller were able to prevent the collision. However, when the discrepancies in the form of parametric variations were included, the SMC provided better results in terms of reduced stopping distance and lower controller effort compared to the full state feedback controller.     

车辆转向与驱动综合控制及横向速度观测器的设计

为了实现复杂工况下车辆自动跟踪控制,建立了纵横向耦合车辆模型,研究了车辆在弯道变速行驶工况的动力学耦合控制问题,根据滑模控制以及动态表面控制理论,提出了一种基于车辆转向与驱动控制的综合控制器,并针对横向车速不可测,设计了横向速度观测器。仿真结果表明该综合控制器具有良好跟踪性能,在复杂工况下表现出较好的动、静态特性。     

双车道公路超车两难区域研究

基于超车行为分析,提出了双车道公路超车行为的两难区域概念,在该区域内超车车辆既无法完成超车动作又不能在避免与对向车辆相撞前安全避让。应用运动学理论建立了下游车队规模、车速、设计车速与两难区域范围以及安全超车视距之间的关系,发现与超越单车的视距要求相比,超越车队所需的安全视距较大,且随着设计车速、下游车队规模以及车速的增大而增大。并发现当流量或车速较大时,两难区域出现的概率较大,且因驾驶者错误估计引发交通事故的机会增多。最后给出了不同下游车队规模条件下安全超车的速度限制及视距要求,为制定安全行车策略以及道路安全管理提供了理论依据。     

面向动态避障的智能汽车滚动时域路径规划

为解决城市低速条件下智能汽车在避障过程中的路径规划问题，提出面向动态避障的智能汽车滚动时域路径规划方法。首先，划分车道可行区域，利用3次拉格朗日插值法拟合车道边界，并根据"车-路"的相对位置关系将车道区域进一步划分为车道间区域与车道内区域两部分。其次，以区域虚拟力场进行动态交通场景模拟，包括在障碍车周身沿车道方向的虚拟矩形区域斥力场，行驶目标位置的虚拟引力场和车道保持虚拟区域引力场3个部分，然后结合划分的车道区域确定各虚拟力场的作用区域。再次，建立主车动力学与运动学模型，障碍车运动学预测模型，把主车与障碍车无碰撞，主车行驶在车道内区域，趋向目标位置以及保证车辆稳定性作为优化目标，综合车辆模型的控制输入、状态变量等动力学约束条件，构建多目标的滚动时域控制器用于车辆避障路径规划，求解获得前轮转角作为控制量。最后，利用MATLAB和veDYNA软件对提出的路径规划控制系统分别在静态障碍和动态障碍工况下进行联合仿真。研究结果表明：该方法能够很好地解决躲避静态障碍和低速动态障碍车的问题，控制车辆驶向目标位置，并且在避障过程中满足车辆的动力学约束，同时又不会与道路边界发生碰撞，保证了车辆的安全性和稳定性。     

基于线性路径跟踪控制的换道避撞控制策略研究

为实现车辆自主避撞，改善道路交通安全状况，提出一种基于线性路径跟踪控制的换道避撞控制策略。为实时确定制动和换道时机，获取跟车状态下自车和前车车速、加速度、相对距离以及驾驶人制动反应时间计算制动安全距离和换道安全距离，并在此基础上分别引入制动危险系数B和换道危险系数S评估制动与换道风险，使得车辆发生追尾碰撞的危险程度和主动干预阈值更直观。根据车辆期望横向加速度和期望横向位移的变化特性，采用5次多项式法规划符合驾驶人换道避撞特性的避撞路径。为保证换道避撞过程中驾驶人的安全舒适，采用最大横向加速度约束换道避撞轨迹。为实现对换道避撞路径的线性跟踪控制，保证车辆的操纵稳定性和横摆稳定性，基于车辆稳态动力学模型建立前馈控制，结合线性反馈控制消除换道路径的位置和横摆角偏差，修正参考路径实现直车道场景追尾避撞控制。仿真和实车交叉验证试验表明：根据车辆期望横向加速度和期望横向位移建立的符合驾驶人换道避撞特性的五次多项式换道路径与驾驶人实际换道避撞路径基本吻合，结合碰撞时间和车间时距的制动避撞控制策略能够在保证车辆行驶安全舒适性的同时有效避免车辆追尾碰撞，减少交通事故的发生。     

Path-following in model predictive rollover prevention using front steering and braking

In this paper vehicle path-following in the presence of rollover risk is investigated. Vehicles with high centre of mass are prone to roll instability. Untripped rollover risk is increased in high centre of gravity vehicles and high-friction road condition. Researches introduce strategies to handle the short-duration rollover condition. In these researches, however, trajectory tracking is affected and not thoroughly investigated. This paper puts stress on tracking error from rollover prevention. A lower level model predictive front steering controller is adopted to deal with rollover and tracking error as a priority sequence. A brake control is included in lower level controller which directly obeys an upper level controller (ULC) command. The ULC manages vehicle speed regarding primarily tracking error. Simulation results show that the proposed control framework maintains roll stability while tracking error is confined to predefined error limit.     

State observer-based sliding mode control for semi-active hydro-pneumatic suspension

This paper proposes an improved virtual reference model for semi-active suspension to coordinate the vehicle ride comfort and handling stability. The reference model combines the virtues of sky-hook with ground-hook control logic, and the hybrid coefficient is tuned according to the longitudinal and lateral acceleration so as to improve the vehicle stability especially in high-speed condition. Suspension state observer based on unscented Kalman filter is designed. A sliding mode controller (SMC) is developed to track the states of the reference model. The stability of the SMC strategy is proven by means of Lyapunov function taking into account the nonlinear damper characteristics and sprung mass variation of the vehicle. Finally, the performance of the controller is demonstrated under three typical working conditions: the random road excitation, speed bump road and sharp acceleration and braking. The simulation results indicated that, compared with the traditional passive suspension, the proposed control algorithm can offer a better coordination between vehicle ride comfort and handling stability. This approach provides a viable alternative to costlier active suspension control systems for commercial vehicles.     

智能汽车横纵向运动控制方法综述

在我国随着人民生活水平的提高,车辆保有量也在呈倍速增长,进而引起了大量的交通安全问题,其中由驾驶员操作不当引起的交通事故约占所有交通事故的75%。而汽车的智能化发展可以很好地解决此类交通安全问题。智能汽车的核心技术主要包括环境感知、行为决策及运动控制三方面。其中运动控制作为智能汽车核心技术之一,有着重要的研究意义。智能汽车的运动控制包括横向控制和纵向控制两部分,对汽车横、纵向运动控制中的多种方法进行了分析介绍,包括模型预测控制、模糊逻辑控制、神经网络的自适应滑膜控制、直接式控制和分层式控制;同时介绍了横纵向耦合实现运动控制的重要性,并分析了其研究现状;最后,对智能汽车运动控制的后续发展方向进行了展望,有助于智能汽车运动控制的进一步优化发展。