Optimal preview game theory approach to vehicle stability controller design

Dynamic game theory brings together different features that are keys to many situations in control design: optimisation behaviour, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In the presented methodology, vehicle stability is represented by a cooperative dynamic/difference game such that its two agents (players), namely the driver and the direct yaw controller (DYC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the DYC control algorithm is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degrees-of-freedom vehicle-handling performance model is put into discrete form to develop the game equations of motion. To evaluate the developed control algorithm, CarSim with its built-in nonlinear vehicle model along with the Pacejka tire model is used. The control algorithm is evaluated for a lane change manoeuvre, and the optimal set of steering angle and corrective yaw moment is calculated and fed to the test vehicle. Simulation results show that the optimal preview control algorithm can significantly reduce lateral velocity, yaw rate, and roll angle, which all contribute to enhancing vehicle stability.     

Control Behaviour of Motorcycle Riders

The control behaviour of motorcycle riders is studied by means of a simulation model for the ridermotorcycle system, which stresses the control actions of the riders. The rider model describes the major steering torque control as well as the rider's own body control actions. This simulation model is applied to a single lane change maneuver and the results of this simulation are compared with the experiments in order to examine its validity.     

Lane-keeping benefits of practical rear axle steer

The classic two-degree-of-freedom yaw-plane or ‘bicycle’ vehicle model is augmented with two additional states to describe lane-keeping behaviour and further augmented with an additional control input to steer the rear axle. A simple driver model is hypothesised where the driver closes a loop on a projected lateral lane position. The driver can select the preview distance to compensate driver/vehicle dynamics, consistent with the ‘cross-over’ model found in the literature. A rear axle steer control law is found to be a function of the front axle steering input and vehicle speed that exhibits stability similar to a positive-real system, while at the same time improving the ability of the driver/vehicle system to track a complex curved lane and improving steady-state manoeuvrability. The theoretically derived control law bears similarity to practical embodiments allowing a deeper understanding of the functional value of steering a rear axle.     

A new shared control for lane keeping and road departure prevention

In recent years, the driver's active assistances have become important features in commercialised vehicles. In this paper, we present one of these features which consists of an advanced driver assistance system for lane keeping. A thorough analysis of its performance and stability with respect to variations in driver behaviour will be given. Firstly, the lateral control model based on visual preview is established and the kinematics model based on visual preview, including speed and other factors, is used to calculate the lateral error and direction error. Secondly, and according to the characteristics of the lateral control, an efficient strategy of intelligent electric vehicle lateral mode is proposed. The integration of the vehicle current lateral error and direction error is chosen as the parameter of the sliding mode switching function to design the sliding surface. The control variables are adjusted according to the fuzzy control rules to ensure that they meet the existence and reaching condition. A new fuzzy logic-based switching strategy with an efficient control law is also proposed to ensure a level of continuous and variable sharing according to the state of the driver and the vehicle positioning on the roadway. The proposed control law acts either at the centre of the lane, as a lane keeping assistance system to reduce the driver's workload for long trips, or as a lane departure avoidance system that intervenes for unintended lane departures. Simulation results are included in this paper to explain this concept.     

用于车道识别的分段切换车道模型

为了使得车道模型既能准确地描述车道形状,又不影响车道识别算法的实时性,提出了一种分段切换车道模型。对近视场区域使用直线模型匹配车道线,对远视场区域则在直线模型和二次曲线模型之间进行切换,以适应有曲率和无曲率道路的识别要求。试验表明,使用分段切换车道模型的车道识别算法能够很好地匹配各种形状道路,并能满足实时性要求。     

智能车辆自由换道模型研究

针对传统的车辆换道模型在换道过程中存在着侧向加速度过大或产生跃变、轨迹曲率不连续以及换道过程起始时刻侧向加速度不为零的问题,以四段式车道变换理论为基础,提出一种新的车辆自由换道轨迹函数,并引入B样条理论对换道轨迹进行再规划,进而建立一种新的高速公路车辆自由换道模型。该模型能够较好的解决传统车道变换模型存在的上述缺陷。给定车辆换道轨迹性能评价参数,利用Matlab仿真计算得到新模型产生的换道轨迹,并与另外两种换道模型产生的轨迹进行对比分析。分析结果验证了该模型的正确性及有效性。     

移线综合评价模型及移线策略分析

用GPS导航装置实测了移线轨迹并建立了相应的多项式位移模型。该位移模型不仅与实测移线轨迹符合得较好，而且符合实际转向盘转角变化规律。移线综合评价模型中分析了移线难度和移线安全性。分析结果表明，移线前提高车速并合理选择移线车距是有利的。     

公交与右转混合型专用道仿真分析及效益评价

为解决设置公交专用道所带来的道路资源利用率低，相邻车道交通压力增大，专用道分时段开启致使社会车辆行驶混乱等问题，基于车种分离思想，提出一种公交车辆与右转车辆混合专用道的组织方式，允许公交车辆与右转社会车辆共用一条车道，以寻求保持公交优先与减少对社会车辆影响的平衡点。为论证该方案的可行性，首先，针对所研究的道路环境，提出了基于流量生成模型与配时优化模型的车道组仿真流程；随后，在考虑红灯时右转车辆行驶特性的前提下，建立了人均延误和车均延误的双指标评价矩阵模型；最后，分别在MATLAB和VISSIM仿真平台上，实现了对传统车道组、公交与右转混合型专用道车道组和公交专用道车道组3种方案的效益评价，并对其中的关键影响因素进行分析。仿真结果表明：所提出的公交与右转混合型专用道车道组的总体车均延误与人均延误在大多情况下处于较低水平，而公交专用道车道组和普通车道组也具有各自的优势区域；公交与右转混合型专用道的车道组织方式可以在保证社会车辆延误不明显增加的情况下，有效确保公交车辆的优先性，在一定条件下具有适用性，在工程实践中可作为公交专用道的过渡或替代方案。     

基于改进RepVGG网络的车道线检测算法

为提高自动驾驶系统中车道线检测的速度和精度,提出了基于可解耦训练状态与推理状态的车道线检测算法.在结构重参数化VGG(RepVGG)主干网络中引入注意力机制压缩-激励(SE)模块,增强对重要车道线信息的特征提取;同时设计并行可分离的辅助分割分支,对局部特征进行建模以提高检测精度.采用行方向位置分类车道线检测方式,在主干...     

Influence of uneven rail irregularities on the dynamic response of the railway track using a three-dimensional model of the vehicle–track system

A mathematical model of the vehicle–track interaction is developed to investigate the coupled behaviour of vehicle–track system, in the presence of uneven irregularities at left/right rails. The railway vehicle is simplified as a 3D multi-rigid-body model, and the track is treated as the two parallel beams on a layered discrete support system. Besides the car-body, the bogies and the wheel sets, the sleepers are assumed to have roll degree of freedom, in order to simulate the in-plane rotation of the components. The wheel–rail interface is treated using a nonlinear Hertzian contact model, coupling the mathematical equations of the vehicle–track systems. The dynamic interaction of the entire system is numerically studied in time domain, employing Newmark's integration method. The track irregularity spectra of both the left/right rails are taken into account, as the inputs of dynamic excitations. The dynamic responses of the track system induced by such irregularities are obtained, particularly in terms of the vertical (bounce) and roll displacements. The numerical model of the present research is validated using several benchmark models reported in the literature, for both the smooth and unsmooth track conditions. Four sample profiles of the measured rail irregularities are considered as the case studies of excitation sources, examining their influences on the dynamic behaviour of the coupled system. The results of numerical simulations demonstrate that the motion of track system is significantly influenced by the presence of uneven irregularities in left/right rails. Dynamic response of the sleepers in the roll direction becomes more sensitive to the rail irregularities, as the unevenness severity of the parallel profiles (quantitative difference between left and right rail spectra) is increased. The severe geometric deformation of the track in the bounce–pitch–roll directions is mainly related to such profile unevenness (cross-level) in left/right rails.     

Validation of an Articulated Vehicle Simulation

Tests were performed on a typical UK articulated vehicle to measure dynamic tyre forces and sprung mass accelerations. The measured road profile data and vehicle response data are used to determine some of the important characteristics of articulated vehicle vibration behaviour. In particular, roll motions and their effect on dynamic tyre forces are examined. The measured data are used to validate two and three-dimensional computer models of the vehicle. Attention is given to modelling the tandem leaf-spring trailer suspension. The conditions under which a two-dimensional model can accurately simulate vehicle behaviour are examined.     

Achieving anti-roll bar effect through air management in commercial vehicle pneumatic suspensions

ABSTRACT

This paper introduces the concept of managing air in commercial vehicle suspensions for reducing body roll. A conventional pneumatic suspension is re-designed to include higher-flow air hoses and dual levelling valves for improving the dynamic response of the suspension to the body roll, which commonly happens at relatively low frequencies. The improved air management allows air to get from the air tank to the airsprings quicker, and also changes the side-to-side suspension air pressure such that the suspension forces can more readily level the vehicle body, much in the same manner as an anti-roll bar (ARB). The results of a multi-domain simulation study in AMESim and TruckSim indicate that the proposed suspension configuration is capable of providing balanced airflow to the truck’s drive-axle suspensions, resulting in balanced suspension forces in response to single lane change and steady-state cornering steering maneuvers. The simulation results further indicate that a truck equipped with the reconfigured suspension experiences a uniform dynamic load sharing, smoother body motion (less roll angle), and improved handling and stability during steering maneuvers commonly occurring in commercial trucks during their intended use.     

基于模糊推理的换车道模型

微观交通仿真在交通系统分析和管理方面是1种安全、有效的工具。在交通微观仿真中,用变换车道表现驾驶员行为是1个非常重要的方面;然而,以往的许多换车道模型并没有考虑驾驶员行为的不确定性和认知性。文中利用模糊推理来表现这种不确定性和认知性,从而使换车道行为更加符合现实。通过实际观察数据与模糊推理的微观仿真模拟的结果比较,表明该模型是可行的,有效的。     

人机混驾交通流交织区换道模型切换控制策略

因交织区的强制换道存在紧迫性, 车辆换道行为在交织区后半段会出现因换道意愿强烈而产生的激进换道行为, 这种微观的换道行为将给交通流带来一定影响; 在人机混驾情形下, 不同类型换道切换控制模型同样可能影响交织区通行能力。在分析人机混驾交通流交织区换道行为特性的基础上, 将换道类型分为保守型换道和激进型换道; 在可接受安全间隙模型的基础上结合自动驾驶车辆间的协同行为, 构建自动驾驶车辆在保守状态下的协同换道模型; 以及在激进型状态下考虑目标车道后车类型影响下, 构建激进型换道模型。通过分析津保立交桥实地调研轨迹数据和NGSIM中US-101交织路段轨迹数据, 分别拟合了保守型、激进型换道模型切换点分布函数; 考虑不同车辆驾驶行为特性及其相互作用, 提出人机混驾条件下换道模型切换控制逻辑决策。以SUMO仿真软件搭建实验平台, 考虑人工驾驶车辆换道模型切换点分布特性, 以优化最大流率、交织区整体车辆运行速度、换道车辆速度等为目标, 确定不同自动驾驶车辆渗透率下自动驾驶车辆的最佳保守型-激进型换道模型切换点。仿真结果显示: 在交织区长度为250 m, 自动驾驶渗透率分别为0.2, 0.5, 0.8时, 自动驾驶换道模型切换点分别在180, 80, 50 m处达到最佳, 即随着自动驾驶渗透率的提高, 换道切换点最佳位置将向交织区入口处逐渐移动, 且在自动驾驶渗透率较低时这种换道切换点的变化较为明显; 在较高渗透率下, 由于协同换道出现频率增高, 自动驾驶强制性换道行为比例降低, 换道模型切换点对交织区通行能力的影响逐渐变小。本项研究对人机混驾条件下高速公路交织区自动驾驶车辆的换道控制提供决策依据      

智能网联环境下基于安全势场理论的车辆换道模型

为有效刻画未来智能网联环境下车辆在换道过程中面临的驾驶风险,保证车辆执行更加安全的换道决策,建立基于安全势场理论的车辆换道模型.首先针对车辆换道过程中所遇到的驾驶风险进行评估,利用势场理论给出车辆行驶过程中不同运动状态下安全势场的空间分布.其次根据换道过程中相关车辆不同安全势场分布情况计算出换道结束时的车间临界距离,相...     

Validation of an Articulated Vehicle Simulation

SUMMARY

Tests were performed on a typical UK articulated vehicle to measure dynamic tyre forces and sprung mass accelerations. The measured road profile data and vehicle response data are used to determine some of the important characteristics of articulated vehicle vibration behaviour. In particular, roll motions and their effect on dynamic tyre forces are examined. The measured data are used to validate two and three-dimensional computer models of the vehicle. Attention is given to modelling the tandem leaf-spring trailer suspension. The conditions under which a two-dimensional model can accurately simulate vehicle behaviour are examined.     

面向多元场景结合GLNet的车道线检测算法

各种复杂环境下路面车道线的高效精确检测是自动驾驶领域中车道偏离预警系统的关键性技术之一。由于车辆实际运行环境的复杂性和路面车道线的多样性，现有方法在车道线检测的准确性和鲁棒性上仍需不断增强。提出一种面向多元场景结合GLNet的车道线检测算法。首先采用改进Gamma校正对待检测路面图像预处理，消减光照不均匀、夜晚等环境干扰，增强车道线纹理。然后为增强数据集的多样性，在LaneNet网络的基础上引入对抗生成网络DCGAN，构建GLNet网络模型。该模型采用编码-解码的网络结构提取车道线特征（车道蒙板和像素点），通过DBSCAN聚类算法将不同车道线划分为不同的实体，使用H-Net网络学习的视觉转换矩阵优化并拟合输出车道线。最后基于已训练好的GLNet权重模型对车道线进行精确提取，并在Tusimple数据集和自制数据集上测试验证。试验结果表明：该方法的检测准确率可达97.4%，相较于基于LaneNet网络的车道线检测算法明显提高；DCGAN网络的加入丰富了数据集类型，并提高了该模型的表征及分类能力；DBSCAN聚类算法的平均聚类时间约为0.016 s，相较于Meanshift算法运行效率更高。所提出的方法考虑了不规范、环境复杂等多种道路类型的车道线检测任务，提升了对复杂噪声与多元场景的处理能力，在车辆辅助驾驶领域具有较好的鲁棒性和适用性。     

基于驾驶人不满度的高速公路自动驾驶换道决策

为了实现高速公路的自由换道行为决策，并满足行车安全高效性、决策结果平稳无震荡、与运动规划模块结合引导车辆行驶等要求，提出了一种基于驾驶人不满度的换道行为决策方法。首先，根据驾驶人的速度期望建立了驾驶人不满度累积模型，并基于驾驶人速度不满累积度产生换道意图。其次，依据不同车道障碍车的运动状态，设计了2种目标车道选择策略，通过预测引擎对各个待选车道进行预测和评估，选取其中行车效率较高的车道作为目标车道，同时建立换道最小安全距离模型，用以在换道全过程中判断换道的可行性。然后，将换道行为决策的结果以目标车道的形式传递给基于改进人工势场的运动规划模块，用于运动规划模块目标的选取，以引导车辆横纵向运动。最后，在CarSim/PreScan/Simulink的联合仿真平台和硬件在环平台上建立多种测试场景，验证换道行为决策算法。试验结果表明：换道行为决策算法能够依据驾驶人速度不满累积度产生稳定的换道意图，进而根据所设计的换道策略选取具有更高行车效率的目标车道，并在换道过程中持续判断换道的可行性，以应对障碍车辆突然加减速等突发状况，保证换道过程的高效性和安全性；换道行为决策算法通过目标车道的转换，引导运动规划模块调整车辆的运动，实现跟车、换道等行为。     

基于相关向量机的换道安全性评估

针对传统换道预警系统算法存在的准确率和可靠率等问题,利用毫米波雷达、AWS视觉传感器、车载陀螺仪等设备搭建了试验车,在普通高速公路上对19名被试人员进行了实际道路驾驶试验,并从试验数据中筛选出近1000次换道行为数据.以3段式换道轨迹模型为基础,将横摆角速度统计值作为确定各个阶段曲率半径的依据,根据横摆角速度α= 0.05的上侧分位数确定可接受安全域的上限;并以换道持续时间的极大值为基准,通过分析12 s以上的换道过程,形成一系列控制点,采用B样条曲线规划确定了可接受安全域的下限.通过利用相关向量机(RVM)模型对换道过程参数进行估计,并利用7次多项式模型对换道轨迹进行拟合,然后将拟合轨迹与可接受安全域上限或者下限所围成面积与可接受安全域面积的比值作为预警参数,实现了对换道安全性的评估.利用真实数据对上述评估算法进行验证,结果表明,所提出的换道安全算法的评估结果能正确反映换道的真实安全性,并且与驾驶人的操作行为具有良好的相关性.      

A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model

The purpose of this study is to propose a cellular automata (CA) traffic flow model with high accuracy for lane change decision and name it LCCAM. A driving simulator experiment was conducted to find factors affecting lane changing decisions. A back-propagate (BP) neural network was used to obtain the lane changing rules for the microscopic lane changing decision model (LCDM), and the collected accurate vehicular trajectory data were used to train the BP neural networks for the prediction of lane changes. After comparing different input variable combinations, the most accurate input setting was determined, including the locations and velocities of neighboring vehicles, inner/outer lane indicator, and the speed limits of the corresponding lane. Then, the determined BP neural network was adopted in the LCCAM as the LCDM. Simulation results showed that the LCCAM can capture important characteristics such as the mean velocities and the number of lane changes well, by comparing with observed traffic flow. Meanwhile, the LCCAM illustrates a better performance in replicating the number of lane changes than the other reference CA models. The research results show that the LCCAM proposed in this study will have potential and value for autonomous driving and active safety analysis in the future.