An innovative localisation algorithm for railway vehicles

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.     

Evaluation of odometry algorithm performances using a railway vehicle dynamic model

In modern railway Automatic Train Protection and Automatic Train Control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. Simplified two-dimensional models of railway vehicles have been usually used for Hardware in the Loop test rig testing of conventional odometry algorithms and of on-board safety relevant subsystems (like the Wheel Slide Protection braking system) in which the train speed is estimated from the measures of the wheel angular speed. Two-dimensional models are not suitable to develop solutions like the inertial type localisation algorithms (using 3D accelerometers and 3D gyroscopes) and the introduction of Global Positioning System (or similar) or the magnetometer. In order to test these algorithms correctly and increase odometry performances, a three-dimensional multibody model of a railway vehicle has been developed, using Matlab-Simulink?, including an efficient contact model which can simulate degraded adhesion conditions (the development and prototyping of odometry algorithms involve the simulation of realistic environmental conditions). In this paper, the authors show how a 3D railway vehicle model, able to simulate the complex interactions arising between different on-board subsystems, can be useful to evaluate the odometry algorithm and safety relevant to on-board subsystem performances.     

Literature review and fundamental approaches for vehicle and tire state estimation*

ABSTRACT

Most modern day automotive chassis control systems employ a feedback control structure. Therefore, real-time estimates of the vehicle dynamic states and tire-road contact parameters are invaluable for enhancing the performance of vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production vehicles are equipped with onboard sensors (e.g. a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors), which when used in conjunction with certain model-based or kinematics-based observers can be used to identify relevant tire and vehicle states for optimal control of comfort, stability and handling. Vehicle state estimation is becoming ever more relevant with the increased sophistication of chassis control systems. This paper presents a comprehensive overview of the state-of-the-art in the field of vehicle and tire state estimation. It is expected to serve as a resource for researchers interested in developing vehicle state estimation algorithms for usage in advanced vehicle control and safety systems.     

基于自适应卡尔曼滤波的轮速信号处理技术

分析了轮速信号的检测误差,建立了轮速估计的系统状态空间模型,采用了用于轮速估计的自适应卡尔曼滤波算法.利用MATLAB进行了仿真,验证了算法的有效性.实车试验表明,滤波后的轮速信号延时小,响应速度快,平滑效果比较理想,可以用来直接估计车速.     

基于双目视觉的前车距离识别方法

本文提出一种基于双目视觉系统,对道路上各对象进行特征识别的方法。首先利用Canny算子和霍夫变换等图像处理方法检测出道路区域,以提高其后的图像处理效率;然后在道路区域内部通过阴影识别方法对车辆进行识别;最后利用双目视觉几何关系,对对象物体距离进行比较。实验结果表明,通过本文所述方法可准确识别出图像中各对象及其距离关系。     

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.     

Estimation of Tire-Road Friction Using Observer Based Identifiers

This paper presents methods for identifying the tire-road friction coefficient. The proposed methods are: an observer-based least square method and an observer/filtered-regressor-based method. These methods were designed assuming that some of the states are not available since physical parameter identification methods developed assuming that the system states are available are not attractive from a practical point of view. The observer is used to estimate signals which are difficult or expensive to measure. Using the estimated states of the system and the filtered-regressor, the parameter estimates are obtained. The proposed methods are evaluated on an eight state nonlinear vehicle/transmission simulation model with a Bakker-Pacejka's formula tire model. Vehicle tests have been performed on dry and wet roads to verify the performance of the methods. It has been shown through simulations and vehicle tests how the RPM sensors can be used with observer based identification methods to estimate the tire-road friction from measurements of engine rpm, transmission output speed and wheel speed. The proposed methods will be useful in the implementation and adaptation of vehicle collision warning/avoidance algorithm since the tire-road friction can be estimated only using the RPM sensors which are currently being used in production vehicles.     

基于车道线虚线角点检测的行车安全视距测算模型

开展行车视距调查对于营运期公路安全评价至关重要,这对车载条件下行车视距检测提出了要求。针对现有基于车道线图像特征点所构建的视距模型精确度不高的问题,提出了1种以车道线虚线角点为关键特征的行车安全视距测算模型。在车载设备获取的图像预处理基础上,采用轮廓跟踪法对车道线虚线轮廓进行提取,通过设定轮廓尖锐度阈值以实现对车道线虚线角点的初步筛选;使用最大、最小距离法对候选角点进行聚类分类,将每类中尖锐度最大的点判定为真实角点;此外,结合车道线虚线图像梯形特征实现对角点的精确提取;根据成像原理的坐标转换关系,通过解构角点在世界坐标和像素坐标之间的映射关系以求解二者之间的转换矩阵,得到实际道路环境的行车安全视距测算模型;将模型所测算的行车视距与运行车速所需的行车视距进行对比,实现对不同道路线形下行车视距的评价。通过实车实验对所提行车视距测算模型进行动态和静态检测精度验证。结果表明：该模型在静态条件下的行车视距检测误差小于7%,低于采用车道线特征点提取方法检测的误差;在动态车载条件可实现行车视距的连续检测,表明在该模型能适应动态条件对行车视距的检测。该模型可实时动态检测行车视距,为营运期公路安全评价提供支撑。     

振动压实引起环境振动的试验研究

根据BW219DH-3振动压路机压实施工诱发地面振动的实测资料，分析得出了振动压实施工诱发的地面振动的特点、振动加速度的主频特性和振动加速度幅值在不同方向上的衰减特性。通过对加速度幅值回归拟合，得到了加速度衰减规律公式，从而给出了控制施工安全距离的指导公式。     

Robust estimation of maximum tire-road friction coefficient considering road surface irregularity

An accurate estimation of the maximum tire-road friction coefficient may provide higher performance in a vehicle active safety control system. Unfortunately, real-time tire-road friction coefficient estimation is costly and necessitates additional sensors that must be installed and maintained at all times. This paper proposes an advanced longitudinal tire-road friction coefficient estimation method that is capable of considering irregular road surfaces. The proposed algorithm uses a stiffness based estimation method, however, unlike previous studies, improvements were made by suggesting a third order model to solve problems related to nonlinear mu-slip curve. To attain the tire-road friction coefficient, real-time normalized force is obtained from the force estimator as exerted from the tire in the low slip region using the recursive least squares method. The decisive aspect of using the suggested algorithm lies in its low cost and versatility. It can be used under irregular road conditions due to its capability of easily obtaining wheel speed and acceleration values from production cars. The newly improved algorithm has been verified to computer simulations as well as compact size cars on dry asphalt conditions.     

Evaluating the effectiveness of a smartphone speed limit advisory application: An on-road study in Port-Harcourt,Nigeria

The development of mobile phone applications that provide speed limit advice and warnings offers opportunities for use of the technology in the improvement of driver safety. This paper looks at the effect of an advisory Intelligent Speed Assistance (ISA) application on driver speeding behaviour. Twenty participants (all males within the age range of 35–60 years) completed a within-group experimental design. Participants drove in real traffic on a 46 km test route which incorporated three-speed limits zones (50 km/h, 60 km/h, and 80 km/h speed limits) and aggregated into 10 different segments. Compared with baseline levels, possible impacts of ISA system functionalities on driver behaviour were studied through appropriate metrics including cumulative speed distribution, mean speed, speed deviation, 85th percentile speed, percentage distanced travelled above the speed limit, and safety benefit estimation. Results indicated the use of the ISA application led to significant improvement in speed limit compliance particularly in the 60 km/h and 80 km/h zones where speeding was eliminated. There were no observed negative effects on driver speeding behaviour from the use of the system. In general, the findings suggest the use of the ISA system, resulted in the adoption of vehicle speeds that are likely to improve road safety.     

基于扩展卡尔曼滤波算法的智能泊车航位推算研究

针对智能泊车系统对高精度车辆航位估计的要求,设计了基于轮速及方向盘转角信号的扩展卡尔曼滤波航位推算算法.离线仿真实验将本算法与传统航位推算算法进行了对比,结果显示本算法具有更高的精度;实车试验进一步验证了本算法的实际应用价值.     

基于红外测距的道路前方车辆探测预警系统研究

进行道路前方车辆探测预警系统设计时,通常采用红外测距仪来获取道路前方车距信息,并以此作为前车探测的基础数据。为了消除系统状态误差和测量误差对车距信息数据精度的影响,可根据车距信息和相对车速不会突变的特性建立预测模型,基于此预测模型,应用Kalman滤波理论准确预测相对车速,并利用车距信息和相对车速计算安全距离报警阈值。试验证明该探测及预警方法可大大提高车辆探测的准确性和鲁棒性。     

Accurate INS position solution during GPS outages or degraded GPS

Temporary degraded GPS (DGPS) position loss, in circumstances such as an overhead bridge, can be alleviated by an inertial navigation system (INS) that uses onboard sensors, such as yaw and speed sensors, to determine vehicle position. This paper introduces a post-processing DGPS/INS integration approach based on using the INS solution during DGPS outages or periods of low accuracy DGPS position solutions. In this approach, the INS solution initialization is performed using the DGPS solution before DGPS position solution loss, and measurements from the Inertial Measurement Unit (IMU). The final post-processed INS solution is a weighted average of the INS forward and backward solutions. This work constitutes two parts: the INS initialization methods for different degrees of freedom vehicle positioning models, and the developed weighting model necessary to combine the forward and the backward solutions. The former part is essential in obtaining acceptable INS initial states for both the stand-alone INS or any post-processing or real time INS/GPS integrated system. The latter part is based on the use of the complementary error behaviours of the backward and the forward solutions, and can be used as a survey method with acceptable position solutions accuracies. Applying the forward/backward INS combined solution method on real data shows that the resultant INS solution accuracy is 35 cm or less over a 1000 m road segment. This method is used to survey freeways interchange road segments where 50% of the surveyed distance has no DGPS solution or has a degraded DGPS solution. The average achieved accuracy over the whole freeways interchange is around 40 cm over a 23 km distance.     

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.     

基于城市主干路交通流数据的跟驰模型标定

根据某种机理建立的交通流模型需要通过模型标定和验证后才能具体应用到实际中.通过采用视频处理技术,对陕西省西安市二环主干路和浙江省舟山市昌洲大道上上下高峰时期内的车辆微观运动录像进行技术处理,提取得到了包括位移、速度和加速度的车辆微观运动轨迹数据.根据这些交通流数据,采用Levnberg-Marquardt算法,分别对跟驰理论中2个典型的跟驰模型,即惯性模型中的敏感系数、安全时间间隔、最小安全车间距、允许速度和智能驾驶人模型中的理想速度、安全时间间隔、静止安全距离、启步加速度和舒适加速度进行了标定和验证.针对惯性模型,当允许速度大于实际速度时,位移均方差和速度均方差的平均值分别为2.8m和0.58 m/s,当允许速度小于实际速度时,位移均方差和速度均方差的平均值分别为2.22m和0.49 m/s;针对智能驾驶人模型,利用早、中、晚3组数据进行标定,得到的位移均方差和速度均方差的平均值分别为0.12m和0.10 m/s,0.07m和0.10 m/s,0.75m和0.27 m/s.因此,惯性模型与智能驾驶人模型都可用于描述城市主干路近饱和状态(即跟随车辆的最大速度远小于允许速度的行驶状态)下的车辆跟驰行为,而且当智能驾驶人模型中的加速度指数取较大的值时,它较前者更为适合.     

Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade

The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.     

Real-Time Longitudinal Location Estimation of Vehicle Center of Gravity

The longitudinal location of a vehicle’s center of gravity (CG) is used as an important parameter for vehicle safety control systems, and can change considerably according to various driving conditions. Accordingly, for the better performance of vehicle safety control systems, it is essential to obtain the accurate CG location. However, it is generally difficult to acquire the value of this parameter directly through sensors due to cost reasons. In this study, a practical algorithm for estimating vehicle’s longitudinal CG location in real time is proposed. This algorithm is derived based only on longitudinal motion of the vehicle, excluding excessive lateral, yaw and roll movements of the vehicle. Moreover, the proposed algorithm has main differences from previous studies in that it does not require information such as vehicle mass, vehicle moments of inertia, road grade or tire-road surface friction, which are difficult to acquire. In the proposed algorithm, the relationship between the ratio of rear-to-front tire longitudinal force and the corresponding wheel slips are used to determine the CG location. To demonstrate a practical use of the proposed algorithm, the ideal brake force distribution is tested. The proposed CG estimation algorithm and its practical use are verified via simulations and experiments using a test vehicle equipped with electro-mechanical brakes in the rear wheels. It is shown that the estimated CG locations are close to the actual ones, and that the deceleration can be maximized by the ideal brake force distribution.     

Accurate INS position solution during GPS outages or degraded GPS

Temporary degraded GPS (DGPS) position loss, in circumstances such as an overhead bridge, can be alleviated by an inertial navigation system (INS) that uses onboard sensors, such as yaw and speed sensors, to determine vehicle position. This paper introduces a post-processing DGPS/INS integration approach based on using the INS solution during DGPS outages or periods of low accuracy DGPS position solutions. In this approach, the INS solution initialization is performed using the DGPS solution before DGPS position solution loss, and measurements from the Inertial Measurement Unit (IMU). The final post-processed INS solution is a weighted average of the INS forward and backward solutions. This work constitutes two parts: the INS initialization methods for different degrees of freedom vehicle positioning models, and the developed weighting model necessary to combine the forward and the backward solutions. The former part is essential in obtaining acceptable INS initial states for both the stand-alone INS or any post-processing or real time INS/GPS integrated system. The latter part is based on the use of the complementary error behaviours of the backward and the forward solutions, and can be used as a survey method with acceptable position solutions accuracies. Applying the forward/backward INS combined solution method on real data shows that the resultant INS solution accuracy is 35 cm or less over a 1000 m road segment. This method is used to survey freeways interchange road segments where 50% of the surveyed distance has no DGPS solution or has a degraded DGPS solution. The average achieved accuracy over the whole freeways interchange is around 40 cm over a 23 km distance.     

基于横向安全距离模型的主动避障算法

现有的安全距离模型是基于纵向相对车速或减速度值建立的,没有考虑移动目标的横向运动特性。本文利用移动目标横穿马路的速度、相对位置,建立横向安全距离模型,并提出一种基于横向安全距离模型的主动避障算法。首先,根据横向移动目标横穿马路的速度、相对位置和自车的制动距离建立横向安全距离模型,设计主动避障算法。接着,为计及路面条件对制动效果的影响,引入当前行驶路面估算的附着系数峰值估算最大制动减速度,约束目标避障减速度,并调整制动强度,以适应不同路况的安全避障行驶。最后,以典型横向移动目标-骑行者作为研究对象,通过PreScan/Simulink/CarSim联合仿真验证避障算法的有效性。结果表明:基于横向安全距离模型的主动避障算法能有效避免与骑行者碰撞,提高行车的主动安全性。