车路协同环境下车辆前照灯自适应控制方法研究

针对现有自适应前照灯控制系统仅仅根据汽车行驶状态及转向盘触发车灯偏转,使得车灯偏转动作带有明显的滞后性的问题,引入车辆行驶前方道路线形信息,建立了车路协同环境下车辆前照灯自适应控制方法的系统。在不改变原有车灯控制系统执行机构的情况下,利用车路协同装置将线形曲率等道路信息提前传输给汽车,综合利用行车状态信息实时计算出车灯偏转角度,自适应调整灯光照明方向。经仿真结果表明,该方法可以更加主动精确的调整车灯偏转角度,减少视野盲区,提高行车安全性。     

高速无人驾驶车辆的操控稳定性研究

通过分析路面附着条件和道路曲率等因素对车辆转向特性和稳定性的影响,建立了高速车辆的等效动力学模型。提出了一种变步长的模型离散化方法,能够在保证车辆模型预测精度的基础上,实现较长的预测时域,并满足计算实时性的要求。通过对高速车辆稳定行驶状态进行分析,推导了基于包络线的滑移稳定性约束条件,并设计了基于模型预测控制的高速无人驾驶车辆的轨迹跟踪控制器。仿真结果表明,该方法可有效保证高速无人驾驶车辆在不同地面附着情况及道路曲率下的操控稳定性。     

车辆自动驾驶系统纵向和横向运动综合控制

为提高车辆自动驾驶系统的运动性能,基于模糊逻辑和滑模控制理论设计了一种车辆纵向和横向运动综合控制系统。该控制系统通过对前轮转向角度、发动机节气门开度、制动液压及主动横摆力矩进行协调控制,使车辆能够以期望速度在理想道路轨迹上行驶,并提高车辆在行驶过程中的操纵稳定性。仿真结果表明:纵向和横向运动综合控制系统能够提高车辆在不同行驶工况下的跟踪性能和运动性能,在车辆自动驾驶过程中是有效的。     

线控转向系统前轮主动转向控制策略研究

文章的研究目的是实现线控转向系统前轮主动转向以改善车辆的行驶状态。文章首先对转向执行模块进行动力学分析,并设计出基于前馈控制的理想传动比;其次,结合理想传动比和状态反馈,建立前馈-反馈联合控制系统,以获得最优的前轮转角;最后,联合Carsim中的车辆模型进行仿真试验,并选取方向盘转角阶跃输入作为试验工况。结果表明,文章所采用的联合控制策略可实时调整前轮转角,有效地改善了车辆的行驶状态,为线控转向系统的研究提供了一定的参考价值。     

一种基于轨迹预判的垂直泊车路径规划算法研究

为解决目前量产自动泊车系统泊车成功率低、泊车完成后车辆姿态偏斜等问题，研究搭建基于阿克曼转向几何学和车辆运动学的车辆运动模型，构建基于可行驶区域的栅格电子地图，在几何路径规划方法中融入基于轨迹预判的碰撞约束方法和路径居中算法，采用车辆膨胀轮廓模型，最大程度利用电子地图可行驶区域和保证路径安全性，设计出一种基于轨迹预判的垂直泊车路径规划算法，并通过仿真测试和实车测试验证算法的可行性。该算法可大大提高泊车成功率，能帮助解决泊车完成后车辆偏斜不居中的问题。     

基于T-S模糊方法的车辆主动悬架多目标控制研究

为了研究因曲线运动引起的车辆侧翻及防测翻控制方法,提升车辆在不平整道路上的平顺性及紧急避障转向操纵下侧倾稳定性,采用Takagi-Sugeno(T-S)模糊建模方法,设计了主动悬架自适应多目标鲁棒控制策略。分析了基于车辆运动状态的模糊隶属度函数选择方法,当车辆直线行驶或动挠度较小时,保证车辆的行驶平顺性,当车辆发生极限转向或动挠度较大时,限制悬架相对运动量,增强对车身的垂向支撑。以优化加速度H_∞性能及悬架动挠度为控制目标,通过使用并行分布补偿方法将结果优化问题转换为线性矩阵不等式求解问题,确定反馈控制增益。采用自适应鲁棒控制(Aaptive Robust Control-ARC)保证系统在非线性、不确定性下,控制力跟踪的鲁棒性。通过SIMULINK~?及CARSIM~?联合仿真对主动悬架平顺性及侧倾稳定性控制效果进行验证,结果表明:该控制方法可以有效提升在良好路面正常行驶工况下车辆的平顺性,和被动悬架相比,小激励工况下,其加速度峰值降低了70%以上,在大激励下动挠度峰值相比被动悬架降低了15%以上。在随机路面输入下,车辆质心加速度均方根值相较被动悬架降低了4%以上,后轴悬架动挠度峰值降低近20%。当车辆发生侧翻危险工况时,基于T-S Fuzzy的主动悬架可以有效地增加车辆悬架支持力,减小车辆侧倾角,避免车辆发生侧翻。     

分布式电动车辆驱动系统MFAC主动容错控制

为解决分布式电驱动车辆驱动系统主动容错控制大多需要依赖于复杂、非线性车辆模型以及精确故障信息这一问题,提出了基于多输入多输出无模型自适应主动容错控制方法。该方法在控制系统设计时仅利用车辆系统的多个输入输出信息,在各个失效工况下,通过驱动系统和转向系统的协同容错控制,保证车辆既能维持期望车速也不偏离既定轨迹行驶,并通过理论推导证明了控制器单调收敛性和有界输入输出。基于MATLAB/Simulink和CarSim的联合仿真对控制算法有效性进行了验证,典型工况下,整车纵向速度误差维持在3%以内,横向不失稳以及不跑偏,确保了行驶安全;在此基础上通过驾驶模拟器实验验证了控制算法的实时性。     

基于高精地图的车辆节能巡航控制方法研究

传统的定速巡航模式可在一定程度上缓解驾驶疲劳,并在平坦道路上使车辆行驶保持较好的燃油经济性,但在具有坡度的道路上,定速巡航往往会导致燃油消耗增多,不利于节能减排。随着车用导航高精地图的不断发展与普及,智能网联车辆可依靠实时地图信息提前获取前方道路坡度及交通流信息,这使得车辆节能巡航成为可能。基于车用导航高精地图,以旅途耗时和总燃油消耗作为代价函数,利用正向动态规划求解节能巡航车速;采用Matlab 软件的 Simulink 工具,构建车辆行驶计算模型,并输入溧宁高速约 10 km 的道路信息进行仿真验证。结果表明：相较于普通定速巡航,基于车用导航高精地图的车辆节能巡航可在通行时间延误不超过 1.24% 的前提下降低 5.95% 的燃油消耗。     

视觉失效条件下车道保持辅助控制策略研究

为保证车道保持辅助系统在视觉失效情况下能够顺利移交车辆控制权,利用目标车方位信息间接估算道路曲率,在纯追踪(Pure Pursuit)算法的基础上,参考车速和道路曲率,利用模糊控制器进行转向修正,并提出了车道保持指数用以评价车道保持性能。构建了硬件在环(HIL)仿真测试平台,利用快速控制原型和某视觉感知系统分别对3种控制策略进行了4种测试场景下的硬件在环测试。测试结果表明,使用改进型Pure Pursuit策略的自车能够有效避免驶出车道,4种测试场景下平均车道保持指数为65.1%,较Pure Pursuit策略和转向盘保持策略分别提高73.9%和135.2%。     

基于变权重系数的分布式驱动无人车轨迹跟踪

为了提高分布式无人车轨迹跟踪的精度，提出了基于自主与差动协调转向控制的轨迹跟踪方法。首先，在车辆三自由度模型基础上，基于模型预测控制（MPC）实时计算前轮转角以控制车辆进行自主转向轨迹跟踪。在此过程中，为了提高自主转向下车辆的轨迹跟踪精度与行驶的稳定性，考虑多种因素，利用经验公式及神经网络控制对MPC的预瞄步数和预瞄步长进行多参数调整，实现预瞄时间的自适应控制。其次，在恒转矩需求的情况下，以轨迹偏差为PID控制器的输入及左右轮毂电机转矩为输出进行差动转向控制，实现了差动转向下的轨迹跟踪控制。然后，通过设置权重系数的方法将自主与差动转向相结合。考虑到车辆横纵向动力学因素，采用模糊控制及经验公式对权重系数进行了调整，从而在提高车辆转向灵活性与轨迹跟踪效果的同时保证车辆行驶的稳定性。CarSim与Simulink联合仿真以及实车试验结果表明：与自主转向轨迹跟踪相比，采用变权重系数的协调控制可以在不同的工况下提高车辆的转向灵活性与轨迹跟踪的精度，轨迹跟踪偏差的均方根值改善率达到了11%。所提出的协调转向控制方法可为分布式驱动车辆转向灵活性的提高及轨迹跟踪精度的改善提供一种新的思路。     

Vehicle dynamics and road curvature estimation for lane departure warning system using robust fuzzy observers: experimental validation

This paper describes a new approach to estimate vehicle dynamics and the road curvature in order to detect vehicle lane departures. This method has been evaluated through an experimental set-up using a real test vehicle equipped with the RT2500 inertial measurement unit. Based on a robust unknown input fuzzy observer, the road curvature is estimated and compared to the vehicle trajectory curvature. The difference between the two curvatures is used by the proposed lane departure detection algorithm as the first driving risk indicator. To reduce false alarms and take into account driver corrections, a second driving risk indicator based on the steering dynamics is considered. The vehicle nonlinear model is deduced from the vehicle lateral dynamics and road geometry and then represented by an uncertain Takagi–Sugeno fuzzy model. Taking into account the unmeasured variables, an unknown input fuzzy observer is proposed. Synthesis conditions of the proposed fuzzy observer are formulated in terms of linear matrix inequalities using the Lyapunov method.     

A Trajectory-Based Map-Matching System for the Driving Road Identification in Vehicle Navigation Systems

Driving road identification is the key issue of a vehicle navigation system that supports various services of intelligent transportation systems. The method for driving road identification is also known as map matching (MM). In spite of the development of MM algorithms, limitations still exist in obtaining the positioning data and preparing candidate roads (CRs) that may result in mismatches in some special difficult road configurations such as flyovers and parallel roads. To overcome the limitations, an integrated trajectory-based MM (tbMM) system is proposed based on the trajectory similarity evaluation method. The system can fuse the information from global positioning systems (GPS) and inertial sensors to generate the vehicle trajectory that represents the vehicle continuous movement in three dimensions. The elevation data of vehicle and roads are involved to enhance the trajectory-based matching process. Also the method employs an optimized mechanism for generating and maintaining CRs. Using the mechanism, separated road segments in a digital map are reorganized in the form of possible driving roads and the topology among them is guaranteed. Moreover, the CRs are obtained considering all the possibilities in determining the driving road so that the valuable historical information can be effectively reserved to provide more reliable matches in ambiguous situations. The tbMM system was evaluated using a number of real-world vehicle-level test datasets in urban areas in Beijing. Also a comparison test was performed to evaluate the driving road identification accuracy against existing MM algorithms. The results show that the tbMM system can provide reliable matches with about 99% accuracy in all the difficult scenarios and outperforms the existing algorithms.     

基于拓扑地图的自主泊车路径协调与优化策略

为了使全局路径与泊车路径无偏差对接,得到曲率连续的可行驶路径,为泊车模式切换提供精准位姿,提出基于拓扑地图的自主泊车路径协调与优化策略.首先,定义一种精简的停车场拓扑地图描述形式与道路拓扑设计原则,通过采集停车场内关键特征的定位数据建立停车场拓扑地图.其次,基于道路拓扑设计原则与泊车规划原则,设计"第1次平滑处理-路径...     

Active front wheel steering model and controller for integrated dynamics control systems

We report a model and controller for an active front-wheel steering (AFS) system. Two integrated dynamics control (IDC) systems are designed to investigate the performance of the AFS system when integrated with braking and steering systems. An 8-degrees-of-freedom vehicle model was employed to test the controllers. The controllers were inspected and compared under different driving and road conditions, with and without braking input, and with and without steering input. The results show that the AFS system performs kinematic steering assistance function and kinematic stabilisation function very well. Three controllers allowed the yaw rate to accurately follow a reference yaw rate, improving the lateral stability. The two IDC systems improved the lateral stability and vehicle control and were effective in reducing the sideslip angle.     

Robust road friction estimation during vehicle steering

Automated vehicles require information on the current road condition, i.e. the tyre–road friction coefficient for trajectory planning, braking or steering interventions. In this work, we propose a framework to estimate the road friction coefficient with stability and robustness guarantee using total aligning torque in vehicle front axle during steering. We first adopt a novel strategy to estimate the front axle lateral force which performs better than the classical unknown input observer. Then, combined with an indirect measurement based on estimated total aligning torque and front axle lateral force, a non-linear adaptive observer is designed to estimate road friction coefficient with stability guarantee. To increase the robustness of the estimation result, criteria are proposed to decide when to update the estimated road conditions. Simulations and experiments under various road conditions validate the proposed framework and demonstrate its advantage in stability by comparing it with the method utilising the wide-spread Extended Kalman Filter.     

Vehicle sideslip estimation and compensation for banked road

The sideslip driving status is of fundamental importance to the stability of a vehicle. This paper presents a practical vehicle sideslip driving status estimation method that uses ESP (electronic stability program) sensors. ESP sensors such as wheel speed, lateral acceleration, yaw rate and steering wheel angle sensors are used to determine the sideslip driving status and distinguish a banked road. This estimation algorithm contains front-rear sideslip and banked road detection methods. The proposed sideslip estimation algorithm was designed to use the analytical redundancy of these sensors and Lagrange interpolation methods. The performance and effectiveness of the proposed estimation and compensation algorithm were investigated using vehicle tests. This paper presents the results of two cases that were used for the experimental verification: a curved flat road and banked road.     

自动驾驶汽车的仿真

随着汽车自动化程度的提高，自动驾驶汽车已成为国内外的研究热点之一。本文设计了一种自动驾驶汽车的模型，它能根据道路的弯曲程度变化实时地计算出车辆的转向角度输入，控制车辆按照预设想道路行吮。     

Modeling and control of an anti-lock brake and steering system for cooperative control on split-mu surfaces

The brake and steering systems in vehicles are the most effective actuators that directly affect the vehicle dynamics. In general, the brake system affects the longitudinal dynamics and the steering system affects the lateral dynamics; however, their effects are coupled when the vehicle is braking on a non-homogenous surface, such as a split-mu road. The yaw moment compensation of the steering control on a split-mu road is one of the basic functions of integrated or coordinated chassis control systems and has been demonstrated by several chassis suppliers. However, the disturbance yaw moment is generally compensated for using the yaw rate feedback or using wheel brake pressure measurement. Access to the wheel brake pressure through physical sensors is not cost effective; therefore, we modeled the hydraulic brake system to avoid using physical sensors and to estimate the brake pressure. The steering angle controller was designed to mitigate the non-symmetric braking force effect and to stabilize the yaw rate dynamics of the vehicle. An H-infinity design synthesis was used to take the system model and the estimation errors into account, and the designed controller was evaluated using vehicle tests.     

Full-slip kinematics based estimation of vehicle yaw rate from differential wheel speeds

Vehicle yaw rate is a key parameter required for various active stability control systems. Accurate yaw rate information may be obtained from the fusion of some on-vehicle sensors and GPS data. In this study, the closed-form expression of the yaw rate–written as a function of front wheel rolling speeds and steering angle–was derived via kinematic analysis of a planar four-wheel vehicle on the assumption of no longitudinal slip at the both front tires. The obtained analytical solution was primarily verified by computational simulation. In terms of implementation, the 1:10th scaled rear-wheel-drive vehicle was modified so that the front wheel rolling speeds and the steering angle could be measured. An inertial measurement unit was also installed to provide the directly measured yaw rate used for validation. Preliminary experiment was done on some extremely random sideslip maneuvers beneath the global positioning using four recording cameras. Comparing with the vision-based and the gyro-based references, the vehicle yaw rate could be well approximated at any slip condition without requiring integration or vehicle and tire models. The proposed cost-effective estimation strategy using only on-vehicle sensors could be used as an alternative way to enhance performance of the GPS-based yaw rate estimation system while the GPS signal is unavailable.     

Autonomous Lateral Control of Vehicles in an Automated Highway System

Lateral control of vehicles in IVHS requires the installation of on-board sensors as well as the installation of roadway hardware such as cables, magnets, etc. Existing control approaches in PATH require road curvature and vehicle lateral position (with respect to the center of the lane) information. Hence these approaches rely on roadway sensors to obtain relative lateral position. These methods will necessitate infrastructural changes to the highway.

This paper introduces the concept of autonomous lateral control or auto-tracking. The method allows us to use only line-of-sight sensor information to effect vehicle control. We present a detailed vehicle model. Controllers have been proposed to demonstrate the effectiveness of the proposed auto-tracking scheme. We also examine the possibilities of using this method for lane change purposes in an automated highway system.