基于车辆加速度数据的互通立交匝道驾驶风险分析

为明确互通立交匝道的运行特性和驾驶风险,在重庆市南山立交和江南立交开展了超过30位被试者的小客车实车驾驶试验,通过Speedbox和Mobileye等车载高精度仪器采集了小客车在4条迂回式匝道上的连续运行数据,包括行驶速度、横向加速度、纵向加速度等,明确了迂回式匝道的车辆运行状态,然后运用表征横、纵向加速度关系的G-G...     

一种汽车ESP仿真模型的研究

基于Pacejka的"魔术公式"轮胎模型,建立了包括汽车纵向与横向移动、横摆、侧倾和4个车轮的转动的8自由度动力学模型.设计了由汽车仿真模型和驱动系统、四通道制动系统、制动踏板、转向盘与油门踏板等实物以及控制器(ESP)等部分组成的半实物仿真平台.以侧向加速度与横摆角速度为仿真控制变量对模型进行仿真测试.仿真与实车测试数据相当接近,为ESP的研究提供了有效的模型.     

Asymptotic sideslip angle and yaw rate decoupling control in four-wheel steering vehicles

This paper shows that, for a four-wheel steering vehicle, a proportional-integral (PI) active front steering control and a PI active rear steering control from the yaw rate error together with an additive feedforward reference signal for the vehicle sideslip angle can asymptotically decouple the lateral velocity and the yaw rate dynamics; that is the control can set arbitrary steady state values for lateral speed and yaw rate at any longitudinal speed. Moreover, the PI controls can suppress oscillatory behaviours by assigning real stable eigenvalues to a widely used linearised model of the vehicle steering dynamics for any value of longitudinal speed in understeering vehicles. In particular, the four PI control parameters are explicitly expressed in terms of the three real eigenvalues to be assigned. No lateral acceleration and no lateral speed measurements are required. The controlled system maintains the well-known advantages of both front and rear active steering controls: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres and improved manoeuvrability. In particular, zero lateral speed may be asymptotically achieved while controlling the yaw rate: in this case comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced. Also zero yaw rate can be asymptotically achieved: in this case additional stable manoeuvres are obtained in obstacle avoidance. Several simulations, including step references and moose tests, are carried out on a standard small SUV CarSim model to explore the robustness with respect to unmodelled effects such as combined lateral and longitudinal tyre forces, pitch, roll and driver dynamics. The simulations confirm the decoupling between the lateral velocity and the yaw rate and show the advantages obtained by the proposed control: reduced lateral speed or reduced yaw rate, suppressed oscillations and new stable manoeuvres.     

基于安全车速差的高速公路特长隧道环境驾驶人行为风险特性研究

为研究高速公路特长隧道环境下驾驶人行为风险特性,选取2座典型特长隧道进行实车试验,通过采集熟练驾驶人和非熟练驾驶人的速度数据,将此作为主观预期车速,结合道路行车环境的客观安全车速,构建基于安全车速差的驾驶人行为风险量化方法。在划分隧道路段为入口段、行车段和出口段的基础上,通过切分行车区间,对比分析出入口段2类驾驶人行为风险变化特性及整个隧道路段和普通高速路段的行为风险变化曲线。结果表明： 1)在隧道内部,相对于非熟练驾驶人,熟练驾驶人表现出更高的行为风险值;在隧道外部,则非熟练驾驶人的行为风险值更高一些。2)所有类型驾驶人在普通高速路段行为风险值最高,在隧道入口段的行为风险值最低。上述结果说明： 在隧道路段,熟悉试验道路的驾驶人车速行为并不安全,行为风险值相对较高。     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

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.     

基于自适应模型预测的智能汽车横向轨迹跟踪控制

当路面附着情况和车辆行驶状态不断变化时,基于恒定侧偏刚度的模型预测控制(MPC)不能考虑轮胎非线性特性的影响,难以保证车辆轨迹跟踪的适应性。为此,提出一种考虑轮胎侧向力计算误差的自适应模型预测控制(AMPC),以提高智能汽车在不确定工况下的轨迹跟踪性能。分析了路面附着系数和垂向载荷对轮胎侧向力的影响,基于平方根容积卡尔曼滤波(SCKF)算法,设计了利用侧向加速度和横摆角速度作为测量变量的前后轮胎侧向力估计器。利用轮胎侧向力线性计算值与估计值的差值计算得到侧偏刚度修正因子,设计了前后轮胎侧偏刚度的自适应修正准则,进而提出了一种基于时变修正刚度的AMPC控制方法。基于CarSim与MATLAB/Simulink联合仿真和硬件在环测试平台,对AMPC控制的有效性和实时性进行了验证。研究结果表明：在不同的路面附着情况和车辆行驶状态下,AMPC控制都能够降低横向位置偏差和航向角偏差,有效提高车辆的轨迹跟踪精度,其控制效果明显优于基于恒定侧偏刚度的标准MPC控制。尤其在低附着工况下,标准MPC控制会因为线性轮胎力的计算误差过大而导致车辆在轨迹跟踪时严重失稳,而AMPC控制通过估计轮胎力修正侧偏刚度依然能够保证车辆稳定有效的跟踪参考轨迹。所提出的AMPC控制在保证控制精度的同时具有良好的实时性,对智能汽车控制系统的设计与优化具有重要参考价值。     

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.     

Nonlinear PI front and rear steering control in four wheel steering vehicles

Vehicle steering dynamics show resonances, which depend on the longitudinal speed, unstable equilibrium points and limited stability regions depending on the constant steering wheel angle, longitudinal speed and car parameters.

The main contribution of this paper is to show that a combined decentralized proportional active front steering control and proportional-integral active rear steering control from the yaw rate tracking error can assign the eigenvalues of the linearised single track steering dynamics, without lateral speed measurements, using a standard single track car model with nonlinear tire characteristics and a non-linear first-order reference model for the yaw rate dynamics driven by the driver steering wheel input. By choosing a suitable nonlinear reference model it is shown that the responses to driver step inputs tend to zero (or reduced) lateral speed for any value of longitudinal speed: in this case the resulting controlled vehicle static gain from driver input to yaw rate differs from the uncontrolled one at higher speed. The closed loop system shows the advantages of both active front and rear steering control: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres, enlarged stability regions, reduced lateral speed and improved manoeuvrability; in addition comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced.

For the designed control law a robustness analysis is presented with respect to system failures, driver step inputs and critical car parameters such as mass, moment of inertia and front and rear cornering stiffness coefficients. Several simulations are carried out on a higher order experimentally validated nonlinear dynamical model to confirm the analysis and to explore the robustness with respect to unmodelled dynamics.     

Nonlinear PI front and rear steering control in four wheel steering vehicles

Vehicle steering dynamics show resonances, which depend on the longitudinal speed, unstable equilibrium points and limited stability regions depending on the constant steering wheel angle, longitudinal speed and car parameters.

The main contribution of this paper is to show that a combined decentralized proportional active front steering control and proportional-integral active rear steering control from the yaw rate tracking error can assign the eigenvalues of the linearised single track steering dynamics, without lateral speed measurements, using a standard single track car model with nonlinear tire characteristics and a non-linear first-order reference model for the yaw rate dynamics driven by the driver steering wheel input. By choosing a suitable nonlinear reference model it is shown that the responses to driver step inputs tend to zero (or reduced) lateral speed for any value of longitudinal speed: in this case the resulting controlled vehicle static gain from driver input to yaw rate differs from the uncontrolled one at higher speed. The closed loop system shows the advantages of both active front and rear steering control: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres, enlarged stability regions, reduced lateral speed and improved manoeuvrability; in addition comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced.

For the designed control law a robustness analysis is presented with respect to system failures, driver step inputs and critical car parameters such as mass, moment of inertia and front and rear cornering stiffness coefficients. Several simulations are carried out on a higher order experimentally validated nonlinear dynamical model to confirm the analysis and to explore the robustness with respect to unmodelled dynamics.     

认知分心对车辆跟驰过程操控安全性的影响

为了研究车辆跟驰过程中驾驶人认知分心与驾驶安全的关系,采用驾驶模拟器构建城市道路车辆跟驰场景,并设计3种难度等级的认知分心次任务,采集35名被试驾驶人在试验过程中的方向盘转角、油门开度、制动踏板力等操作参数,以及车辆位置、速度、加速度等车辆运动参数。采用重复测量一般线性模型,分析不同等级认知分心对上述参数的影响。研究结果表明：在横向操控方面,随着认知分心程度增高,方向盘回转率增大,但车辆横向位置标准差减小,表明驾驶人处于认知分心时,采取频繁修正方向盘的补偿方式,降低车辆横向位置波动,过度补偿车辆横向安全性,且该补偿行为与认知分心程度正相关;在纵向操控方面,认知分心时,油门开度、制动踏板位置方差增大,且制动踏板位置均值增大,同时车头间距及时距未观察到显著性变化,表明认知分心时驾驶人采取频繁操作油门、制动踏板,增大制动幅度等方式进行补偿,使车头间距及车头时距等表征车辆纵向跟车安全性参数处于正常驾驶水平,但加速度标准差增大,表明跟车稳定性降低。研究结果为涉及分心的人车交互装置优化设计及考虑分心状态的驾驶人状态管理系统开发提供了一定的理论依据。     

Drowsy behavior detection based on driving information

Drowsy behavior is more likely to occur in sleep-deprived drivers. Individuals’ drowsy behavior detection technology should be developed to prevent drowsiness related crashes. Driving information such as acceleration, steering angle and velocity, and physiological signals of drivers such as electroencephalogram (EEG), and eye tracking are adopted in present drowsy behavior detection technologies. However, it is difficult to measure physiological signal, and eye tracking requires complex experiment equipment. As a result, driving information is adopted for drowsy driving detection. In order to achieve this purpose, driving experiment is performed for obtaining driving information through driving simulator. Moreover, this paper investigates effects of using different input parameter combinations, which is consisted of lateral acceleration, longitudinal acceleration, and steering angles with different time window sizes (i.e. 4 s, 10 s, 20 s, 30 s, 60 s), on drowsy driving detection using random forest algorithm. 20 s-size datasets using parameter combination of accelerations in lateral and longitudinal directions, compared to the other combination cases of driving information such as steering angles combined with lateral and longitudinal acceleration, steering angles only, longitudinal acceleration only, and lateral acceleration only, is considered the most effective information for drivers’ drowsy behavior detection. Moreover, comparing to ANN algorithm, RF algorithm performs better on processing complex input data for drowsy behavior detection. The results, which reveal high accuracy 84.8 % on drowsy driving behavior detection, can be applied on condition of operating real vehicles.     

基于驾驶行为和视觉特性的长大隧道突起路标作用效果评估

突起路标（RPMs）是高速公路隧道内常见的交通安全设施，对预防和减少交通事故具有重要作用。针对高速公路长大隧道RPMs作用效果，从驾驶人角度出发，基于驾驶模拟试验测试，分析RPMs对驾驶人的影响，实现以微观驾驶行为和眼动数据为基础的高速公路长大隧道RPMs作用效果评估。设计2种RPMs方案（RPMs-B：设置在隧道检修道和车道边缘线；RPMs-C：设置在车道分界线和边缘线）与未设置RPMs（RPMs-A）的隧道形成对比；招募32名持照驾驶人，获取细粒度驾驶行为及眼动数据，选择平均速度、加速度、横向偏移等7项指标构建评价指标体系；利用重复测量方差分析方法，探究各项指标的显著性及效应量水平，进而采用基于熵权法的模糊综合评价模型，对比3种RPMs方案的作用效果。研究结果表明：RPMs对驾驶行为及心理、生理具有较显著的作用，具备不同程度的现实意义；驾驶人心理、生理负荷程度降低，焦虑情绪得到缓解，驾驶舒适性良好；速度调控能力增强，对车辆横向位置感知及操纵水平上升，车辆行驶更加平稳；在隧道曲线段RPMs使得平均速度明显增加，增加了事故风险；模糊综合评估结果显示在隧道全程、曲线段和中间段，RPMs-C均为最优方案，即RPMs设置在车道分界线及两侧边缘线位置。研究结果为隧道安全设施的评估及优化问题提供了解决方案，也为进一步形成隧道安全设施设置指南奠定了基础。     

A driver-adaptive stability control strategy for sport utility vehicles

Conventional vehicle stability control (VSC) systems are designed for average drivers. For a driver with a good driving skill, the VSC systems may be redundant; for a driver with a poor driving skill, the VSC intervention may be inadequate. To increase safety of sport utility vehicles (SUVs), this paper proposes a novel driver-adaptive VSC (DAVSC) strategy based on scaling the target yaw rate commanded by the driver. The DAVSC system is adaptive to drivers’ driving skills. More control effort would be exerted for drivers with poor driving skills, and vice versa. A sliding mode control (SMC)-based differential braking (DB) controller is designed using a three degrees of freedom (DOF) yaw-plane model. An eight DOF nonlinear yaw-roll model is used to simulate the SUV dynamics. Two driver models, namely longitudinal and lateral, are used to ‘drive’ the virtual SUV. By integrating the virtual SUV, the DB controller, and the driver models, the performance of the DAVSC system is investigated. The simulations demonstrate the effectiveness of the DAVSC strategy.     

An adaptive lateral preview driver model

Successful modelling and simulation of driver behaviour is important for the current industrial thrust of computer-based vehicle development. The main contribution of this paper is the development of an adaptive lateral preview human driver model. This driver model template has a few parameters that can be adjusted to simulate steering actions of human drivers with different driving styles. In other words, this model template can be used in the design process of vehicles and active safety systems to assess their performance under average drivers as well as atypical drivers. We assume that the drivers, regardless of their style, have driven the vehicle long enough to establish an accurate internal model of the vehicle. The proposed driver model is developed using the adaptive predictive control (APC) framework. Three key features are included in the APC framework: use of preview information, internal model identification and weight adjustment to simulate different driving styles. The driver uses predicted vehicle information in a future window to determine the optimal steering action. A tunable parameter is defined to assign relative importance of lateral displacement and yaw error in the cost function to be optimized. The model is tuned to fit three representative drivers obtained from driving simulator data taken from 22 human drivers.     

Multi-axle vehicle dynamics stability control algorithm with all independent drive wheel

The stability driving characteristic and the tire wear of 8-axle vehicle with 16-independent driving wheels are discussed in this paper. The lateral stability of 8-axle vehicle can be improved by the direct yaw moment which is generated by the 16 independent driving wheels. The hierarchical controller is designed to determine the required yaw torque and driving force of each wheel. The upper level controller uses feed-forward and feed-backward control theory to obtain the required yaw torque. The fuzzification weight ratio of two control objective is built in the upper level controller to regulate the vehicle yaw and lateral motions. The rule-based yaw moment distribution strategy and the driving force adjustment based on the safety of vehicle are proposed in the lower level controller. The influence of rear steering angle is considered in the distribution of driving force of the wheel. Simulation results of a vehicle double lane change show the stability of 8-axle vehicle under the proposed control algorithm. The wear rate of tire is calculated by the interaction force between the tire and ground. The wear of tire is different from each other for the vehicle with the stability controller or not.     

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.     

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。     

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative, as the SVM methodology has not previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM's prediction performance in terms of minimization of the number of false positive prediction errors. Experiment data generated by VIRTTEX, a hydraulically powered, 6-degrees-of-freedom moving base driving simulator at Ford Motor Company, were used. All the vehicle variables were sampled at 50 Hz and there were 16 drowsy drivers (about 3 hours of driving per subject) and six control drivers (approximately 20 minutes f driving each). In total, 3,508 unintentional lane departures occurred for the drowsy drivers and 23 for the control drivers. Our study involving these 22 drivers with a total of more than 7.5 million prediction decisions demonstrates that (a) excellent SVM prediction performance, measured by numbers of false positives (i.e., falsely predicted lane departures) and false negatives (i.e., lane departures failed to be predicted), was achieved when the prediction horizon was 0.6 seconds or less, (b) lateral position and lateral velocity worked the best as SVM input variables among the nine variable sets that we explored, and (c) the radial basis function performed the best as the SVM kernel function.