Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments

Good estimates of vehicle mass and road grade are important in automation of heavy duty vehicles, vehicle following manoeuvres or traditional powertrain control schemes. Recursive least square (RLS) with multiple forgetting factors accounts for different rates of change for different parameters and thus, enables simultaneous estimation of the time-varying grade and the piece-wise constant mass. An ad hoc modification of the update law for the gain in the RLS scheme is proposed and used in simulation and experiments. We demonstrate that the proposed scheme estimates mass within 5% of its actual value and tracks grade with good accuracy provided that inputs are persistently exciting. The experimental setups, signals, their source and their accuracy are discussed. Issues like lack of persistent excitations in certain parts of the run or difficulties of parameter tracking during gear shift are explained and suggestions to bypass these problems are made.     

Development of algorithms for commercial vehicle mass and road grade estimation

Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.     

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic ‘input–output’ model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilised as ‘virtual measurements’ considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.     

Development of estimation algorithms for vehicle’s mass and road grade

In controlling the longitudinal motion of electrified vehicles such as hybrid vehicles and PHEV (Plug-in Hybrid Electric Vehicles), the variation of the driving resistance loads (or driving loads) such as road grade and actual vehicle mass, is the most influential factor which limits the control performance. Measuring the driving load is not impossible, but it is costly since additional sensors have to be mounted on the vehicle. In this study, methods for estimating vehicle mass and road grade are designed to compensate for the driving loads. The proposed methods are verified using simulation tools and then evaluated experimentally.     

A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty

The ability of ground vehicles to quickly and accurately analyse their dynamic response to a given input is critical to their safety and efficient autonomous operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this uncertainty must be considered in the analysis of vehicle motion dynamics. Here, polynomial chaos approaches that explicitly consider parametric uncertainty during modelling of vehicle dynamics are presented. They are shown to be computationally more efficient than the standard Monte Carlo scheme, and experimental results compared with the simulation results performed on ANVEL (a vehicle simulator) indicate that the method can be utilised for efficient and accurate prediction of vehicle motion in realistic scenarios.     

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.     

基于转速辨识的EPS阻尼控制策略的研究

将基于递推最小二乘算法的转速在线辨识方法引入电动助力转向系统中,以估计助力电动机的转速。利用估计转速结合车速,设计了相应的阻尼特性,提出了阻尼控制策略,并对转速辨识和阻尼特性进行仿真。结果表明,利用递推算法进行转速辨识成本低,可靠性、鲁棒性高;提出的阻尼控制策略有效可行。     

Vehicle velocity estimation using effective inertia for an in-wheel electric vehicle

In this study, a vehicle velocity estimation algorithm for an in-wheel electric vehicle is proposed. This algorithm estimates the vehicle velocity using the concept of effective inertia, which is based on the motor torque, the angular velocity of each wheel and vehicle acceleration. Effective inertia is a virtual mass that changes according to the state of a vehicle, such as acceleration, deceleration, turning or driving on a low friction road. The performance of the proposed vehicle velocity estimation algorithm was verified in various conditions that included straight driving, circle driving and low friction road driving using the in-wheel electric vehicle that was equipped with an in-wheel system in each of its rear wheels.     

Hierarchical Scheme of Vehicle Detection and Tracking in Nighttime Urban Environment

In this paper, we propose a novel hierarchical scheme for detection and tracking of vehicles using a vehicle-mounted camera in nighttime under urban environment, where a vehicle can be represented by a pair of taillights and various types of lights are commonplace. The proposed scheme, therefore, mainly focuses on devising robust detection and pairing of taillights in spite of their inherent diversity and continuous transformation in appearance. Thus the appearance symmetry, which many conventional methods rely on, for paring is not guaranteed to be available all the times. Each of the three layers in the scheme is devised to identify a vehicle from individual lights and clutters detected in a hierarchical manner. Robust detection of a pair of taillights, which can be regarded as a vehicle, is sought by successive groupings of the components in a layer and checking not only the intra-layer but the inter-layer relations between them. A structural Kalman filter is employed to maintain the temporal consistency in the motion of the components and their relations as well. Exploiting such relational information increases accuracy in tracking of individual components by reducing effects from fluctuation in positions and shapes, and eventually compensating possible failures in detection of them. As a result, the proposed scheme achieves enhancement in detection and tracking of vehicles in nighttime as proven by experiments on videos including crowded urban traffic scenes.     

用于车辆自主导航的多传感器数据融合方法

提出了一种用于车辆自主导航的多传感器数据磁合方法.首先分析了车辆自主导航过程的态势与威胁,建立了车辆行驶某时刻场景模型.然后综合现有各数据融合模型的优势,运用混合、分层的融合结构改进自主行驶的有效性和准确性.最后将该方法集成到湖南大学自主研制的无人驾驶汽车测试平台上.在城市道路的实际场景中,初步实现了基于路线和交通锥形标的自主导航和行驶任务,车辆自主行驶的融合决策准确率可达到97.5%,表明该方法具有可接受的性能.     

Slip regulation for anti-lock braking systems using multiple surface sliding controller combined with inertial delay control

In this paper, a multiple surface sliding controller is designed for an anti-lock braking system to maintain the slip ratio at a desired level. Various types of uncertainties coming from unknown road surface conditions, the variations in normal force and the mass of the vehicle are estimated using an uncertainty estimation technique called the inertial delay control and then the estimate is used in the design of the multiple surface sliding controller. The proposed scheme does not require the bounds of uncertainties. The ultimate boundedness of the overall system is proved. The proposed scheme is validated by simulation under various scenarios of road friction, road gradient and vehicle loading followed by experimentation on a laboratory anti-lock braking set-up for different friction conditions.     

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.     

Hierarchical map representation using vector maps and geometrical maps for self-localization

Map-based self-localization estimates the pose of the self-driving vehicle in an environment, becoming an essential part of autonomous driving tasks. Generally, maps used in self-localization have detailed geometric information on an environment in formats such as point cloud maps and Gaussian mixture model (GMM) maps. As other maps are widely developed for autonomous driving, vector maps store more object-focused information, such as buildings and road facilities, for navigation and scene understanding in autonomous driving tasks. However, it is not compatible with self-localization due to the lack of detailed geometric information. The two different map formats of vector maps and maps for self-localization complicate the management, preventing the development of the area where a self-driving vehicle can drive stably. This paper proposes a unified map format with a hierarchical structure that enables both vector maps and self-localization maps (i.e., GMM maps) to be managed more easily. Because proposed maps can be treated as vector maps at the high-level layer, various tasks related to navigation and scene understanding in autonomous driving can utilize. A GMM map is stored at the low-level layer associated with a vector map component, enabling accurate self-localization in an environment. The proposed map format is compatible with vector maps widely developed by mapping companies on the surface and facilitates future map management. The experimental results of self-localization in urban areas showed that the proposed map gives the competitive self-localization accuracy compared with the GMM map even with fewer cells that link to vector components. The proposed maps enable self-localization with sufficient accuracy for safe autonomous driving operations.     

中国公路车-桥耦合振动车辆模型研究（双语出版）

为了提出适用于中国车-桥耦合振动分析的车辆动力分析模型,首先基于中国桥梁规范中的设计车辆荷载,结合大量调查统计数据和等效静力分析方法,初步拟定车辆动力分析模型的几何尺寸、质量、刚度、阻尼等参数取值,并与国内外广泛采用的几种车辆模型的参数取值进行对比。接着选取4座钢筋混凝土简支梁桥并建立其三维有限元模型,基于车-桥耦合振动数值模拟分析车辆模型的刚度、阻尼等参数对桥梁上动力冲击系数的影响,并对比几个不同车辆模型对动力冲击系数的影响。最后,选择中国湖南省境内一座实桥和几辆不同轴数的重车开展实桥试验,将实测动力冲击系数与所提车辆模型数值模拟获得的冲击系数进行对比。结果表明：动力冲击系数随车辆总质量的增大而减小,随车辆整体刚度的增大而增大,但随车辆整体阻尼的增大呈先减小后增大的趋势;单个车轴的刚度和阻尼对动力冲击系数的影响不明显;车辆总质量是导致不同车辆模型作用下动力冲击系数差异的主要因素;数值模拟结果与实测结果吻合良好,验证了所提车辆模型及参数取值的合理性;该车辆模型可用于中国的设计车辆荷载作用下桥梁的动力响应分析和相关研究,也可用于估算重量相当的不同类型车辆对桥梁的动力冲击效应。     

基于冲压成形前后网格变化研究汽车碰撞仿真中的冲压效应

为提高碰撞仿真的精度而研究汽车车身钣件在成形过程中的冲压效应,提出了根据冲压前后网格的变形计算出板料的厚度变化、应变分量和等效应变的设想,并相应地推导出计算公式和开发了一个FORTRAN程序。通过以一拉深零件成形为例与有限元软件LS-DYNA的分析进行对比,两者的结果相当接近。表明该设想是可行的,计算结果文件可直接供碰撞仿真使用。     

基于交互多模型的车辆质量与道路坡度估计（双语出版）

车辆结构参数和道路环境信息的实时准确获取是提高智能汽车运动控制性能的重要因素之一，而车辆质量与道路坡度信息是多种汽车控制系统的必要信息，因此质量与坡度在线估计的研究一直受到关注。针对车辆质量与道路坡度的联合估计问题，提出了一种基于交互多模型的质量与坡度融合估计方法。首先，设定了适宜进行质量精确估计的工况条件，据此提出了基于模糊规则的质量估计置信度因子计算算法，进而设计了基于置信度因子的递推最小二乘车辆质量估计算法，以实现质量的在线估计。然后，以车辆纵向动力学模型为基础，建立了运动学和动力学2种坡度估计模型，并设计了基于运动学模型的线性卡尔曼滤波坡度观测器，基于电子稳定性程序ESP的纵向加速度信息实现坡度估计，设计了基于动力学模型的无迹卡尔曼滤波坡度观测器，基于ESP和发动机管理系统EMS的力信息实现坡度估计。运动学模型未考虑车辆姿态信息，坡度估算结果与实际值有偏差；动力学模型对模型精度要求高，算法稳定性差，为充分发挥2种方法优势实现坡度的精确估计，采用交互多模型算法实现了2种坡度估计方法的加权融合。最后，对所设计的算法进行了实车试验验证。结果表明：所设计的质量与坡度估算算法具有较好的实时性和准确性，适合智能汽车运动控制的应用需求。     

一种基于特征的车辆检测方法

提出了一种应用单目视觉进行车辆检测的方法。该方法以车辆阴影以及边缘作为检测的主要特征。在图像预处理中采用自适应双阈值以满足不同光照条件下的使用要求;利用能量密度验证提高车辆垂向边界识别的准确性;用可变模型用来规划车辆检测范围,以满足不同距离车辆的检测需要。为提高车辆检测的准确性及效率,在算法中融合了雷达的探测数据。试验验证表明,该方法有较高的车辆检测准确率,并且能满足智能车应用中的实时性要求。     

基于图像识别轮胎变形的非接触式车辆称重方法

为了解决接触式车辆称重方法存在的安装和维修成本高、使用年限短、识别精度低等问题,创新性地提出一种基于计算机视觉获取轮胎变形的非接触式车重识别方法。首先,利用视频图像采集装置拍摄车辆轮胎图像信息,通过图像处理技术提取轮胎轮廓,并根据轮廓变形计算轮胎的垂向挠度。其次,通过胎压监测系统(TPMS)获取轮胎的真实胎压值,对于没有安装TPMS的车辆,则可以通过图像字符识别技术读取轮胎侧壁的胎压标识信息,再利用统计回归方式确定实际胎压值。在此基础上,将轮胎垂向挠度和胎压值代入推导的称重公式计算轮胎承受的重量,再将所有轮胎承受重量求和得到车辆总重量。最后,以现场的乘用车和重载货车为例,验证在不同胎压和重量变化下非接触式车辆称重方法的准确性,并对比分析3个称重公式的准确性。研究结果表明:车重识别准确率随着胎压增大而降低,随着车重增大而上升;轮胎刚度拟合公式的载重识别准确率达到95%以上,高于理论推导公式和半经验拟合公式。提出的非接触式车辆称重方法具有测量范围广、无需任何额外传感设备、不用封闭交通和易于信息集成等优势,有效地突破了现有接触式车重识别技术的瓶颈,具有很好的工程应用前景。     

A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization

In this research, a hybrid dead reckoning error correction scheme is developed based on extended Kalman filter (EKF) and map matching (MM) to improve the positioning accuracy for vehicle self-localization. The developed method aims at obtaining accurate positions when the GPS signals are occasionally unavailable or weakened. First, the heading data collected from an odometer and an optical fiber gyroscope are integrated by an EKF to reduce the random errors in dead reckoning. Then a modified topological MM algorithm is developed to reduce the systematic errors in dead reckoning. In this work, both cross-track errors and along-track errors are considered to improve positioning accuracy of MM. The errors are finally corrected using the results achieved from both the dead reckoning and the MM when the driving distance of a vehicle exceeds a predefined length or the vehicle turns in an intersection. Experiments have been conducted to evaluate the developed method and the results show that the maximum error and average error of dead reckoning can be respectively reduced to 15.4?m and 5.2?m during the experiment with total distance of 43?km. This positioning accuracy is even better than the accuracy of the low-cost GPSs which are usually at the order of 15–20?m (95%). The developed method is effective to achieve the positions of the vehicle when the GPS signals are occasionally unavailable or weakened.     

Brain limbic system-based intelligent controller application to lane change manoeuvre

This paper presents the application of a novel neuromorphic control strategy for lane change manoeuvres in the highway environment. The lateral dynamics of a vehicle with and without wind disturbance are derived and utilised to implement a control strategy based on the brain limbic system. To show the robustness of the proposed controller, several disturbance conditions including wind, uncertainty in the cornering stiffness, and changes in the vehicle mass are investigated. To demonstrate the performance of the suggested strategy, simulation results of the proposed method are compared with the human driver model-based control scheme, which has been discussed in the literature. The simulation results demonstrate the superiority of the proposed controller in energy efficiency, driving comfort, and robustness.