Evaluation of antilock braking system with an integrated model of full vehicle system dynamics

Antilock braking system (ABS), traction control system, etc. are used in modern automobiles for enhanced safety and reliability. Autonomous ABS system can take over the traction control of the vehicle either completely or partially. An antilock braking system using an on–off control strategy to maintain the wheel slip within a predefined range is studied here. The controller design needs integration with the vehicle dynamics model. A single wheel or a bicycle vehicle model considers only constant normal loading on the wheels. On the other hand, a four wheel vehicle model that accounts for dynamic normal loading on the wheels and generates correct lateral forces is suitable for reliable brake system design. This paper describes an integrated vehicle braking system dynamics and control modeling procedure for a four wheel vehicle. The vehicle system comprises several energy domains. The interdisciplinary modeling technique called bond graph is used to integrate models in different energy domains and control systems. The bond graph model of the integrated vehicle dynamic system is developed in a modular and hierarchical modeling environment and is simulated to evaluate the performance of the ABS system under various operating conditions.     

Evaluation of antilock braking system with an integrated model of full vehicle system dynamics

Antilock braking system (ABS), traction control system, etc. are used in modern automobiles for enhanced safety and reliability. Autonomous ABS system can take over the traction control of the vehicle either completely or partially. An antilock braking system using an on–off control strategy to maintain the wheel slip within a predefined range is studied here. The controller design needs integration with the vehicle dynamics model. A single wheel or a bicycle vehicle model considers only constant normal loading on the wheels. On the other hand, a four wheel vehicle model that accounts for dynamic normal loading on the wheels and generates correct lateral forces is suitable for reliable brake system design. This paper describes an integrated vehicle braking system dynamics and control modeling procedure for a four wheel vehicle. The vehicle system comprises several energy domains. The interdisciplinary modeling technique called bond graph is used to integrate models in different energy domains and control systems. The bond graph model of the integrated vehicle dynamic system is developed in a modular and hierarchical modeling environment and is simulated to evaluate the performance of the ABS system under various operating conditions.     

基于15自由度动力学模型的ABS仿真实验研究

车辆仿真模型的精度是防抱死系统仿真研究的关键环节,控制逻辑是防抱死系统关键技术之一.详细描述了15自由度车辆动力学模型,与实车试验对比完成了模型验证工作.介绍了专业车辆动力学仿真软件veDYNA,并基于仿真环境开发了基于四相位控制逻辑的ABS控制软件,进行了干沥青路面的仿真实验研究.实验结果证明:15自由度动力学模型可以很好的用于ABS技术研究,ABS四相位控制逻辑能较好地发挥系统的性能.四相位控制逻辑也能用于ESP技术的研究.     

车辆特性与ABS控制算法的适应性研究

针对车辆防抱死制动系统(ABS)控制算法的选择及适用问题,提出控制算法的选择应与车辆结构及实际性能要求相结合的方法,可以充分发挥不同ABS控制算法的优越性。使用Matlab/Simulink仿真软件建立车辆单轮制动模型,针对不同的初始制动车速和质心位置向后移动两种情况,分别采用变结构滑模控制和PID控制进行了制动过程的模拟与研究。仿真结果表明,当车辆结构及制动条件发生变化时,两种控制方式都能很好的完成制动,且各有优点,可以满足不同制动性能的需求。     

基于多尺度特征融合CNN模型的车辆精细型号识别

车辆精细型号是车辆识别的主要线索之一,也是智能交通系统的重要组成部分。针对车辆精细型号种类繁多、车辆所处环境复杂多变等因素,提出一种基于多尺度特征融合的车辆精细型号识别方法。该方法基于传统的卷积神经网络,通过提取并融合来自网络底层和高层的车辆特征,完成对车辆精细型号的识别。与其他基于卷积神经网络的车辆精细型号识别方法相比,该方法在提高分类准确率的同时还大幅度降低了整体网络的参数规模。实验结果表明,在公开数据集CompCars的监控场景下其识别精度达到了98.43%,且模型参数大小仅为3.93 MB,平均每张图片只需0.83 ms的分类时间。     

ABS传感器模型及多传感器信息融合在车辆定位中的应用

推导了ABS里程计的数学模型,针对其非线性特点给出了扩展式卡尔曼滤波算式.基于各传感器工作原理分析了系统的采样规律.应用多传感器信息融合理论构建了联邦滤波器定位系统结构.跑车实验表明,无论在GPS卫星短暂屏蔽还是较长时间无卫星信号情况下,系统均能正常有效工作,可满足实际定位导航的要求.     

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

Real-time multibody vehicle model with bush compliance effect using quasi-static analysis for HILS

Vehicle handling simulations such as J-turns, slaloms, double lane changes, and fish hook maneuvering are essential to evaluate the control subsystems of the intelligent vehicle in the HILS (Hardware-in-the loop simulation). It is well known that the compliance effects from bushes in the suspension subsystem are important to have reliable solutions for the handling simulations. In the multibody vehicle dynamics model, the bush elements in the suspension subsystem are usually modeled as high-stiffness springs. However, due to this high stiffness of the bush model, it requires small step-sizes in the numerical integration. With this kind of bush model, it is almost impossible to achieve real-time simulations for HILS. In this paper, a quasi-static analysis method is presented in order to consider bush compliance effects for a real-time multibody vehicle model. To validate the proposed method, the quarter car simulations of the McPherson strut and the Multilink suspension subsystems have been performed, comparing with the ADAMS simulations. Full car bump run simulations and fish hook handling test simulations have also been carried out to see the effectiveness of the proposed model. The CPU time measured shows the real-time capabilities of the proposed method.     

Cooperative vehicle path generation during merging using model predictive control with real-time optimization

This paper proposed a cooperative merging path generation method for vehicles to merge smoothly on the motorway using a Model Predictive Control (MPC) scheme which optimizes the motions of the relevant vehicles simultaneously. The cooperative merging is a merging in where the most relevant vehicle in the main lane would accelerate or decelerate slightly to let the merging vehicle merge in easily. The proposed path generation algorithm can generate the merging path ensuring the merging vehicle can access the whole acceleration area, and do not exceed it. We have introduced a state variable to the optimization problem by which the merging point for the merging vehicle is optimized. The simulation results showed that the cooperative merging path can be successfully generated under some typical traffic situations without re-adjustment of the optimization parameters.     

Full field measurement at the micro-scale using micro-interferometry

This paper presents micro-interferometry as a measurement technique to extract temperature profiles and/or mass transfer gradients rapidly and locally in micro-devices. Interferometry quantifies the phase change between two or more coherent light beams induced by temperature and/or mass concentration. Previous work has shown that temporal noise is a limiting factor in microscale applications. This paper examines phase stepping and heterodyne phase retrieval techniques with both CCD and CMOS cameras. CMOS cameras are examined owing to the high speed at which images can be acquired which is particularly relevant to heterodyne methods. It is found that heterodyne retrieval is five times better than phase stepping being limited to 0.01 rad or λ/628. This is twice the theoretical limit of λ/1,000. The technique is demonstrated for mixing in a T-junction with a 500 μm square channel and compared favourably to a theoretical prediction from the literature. Further issues regarding application to temperature measurements are discussed.     

A dynamic model of an underwater vehicle with a robotic manipulator using Kane's method

Development of a robust autonomous Underwater Robotic Vehicle (URV) is a key element to the exploitation of marine resources. An accurate dynamic model is important for both controller design and mission simulation, regardless of the control strategy employed. In this paper, a dynamic model for an underwater vehicle with an n-axis robot arm is developed based on Kane's method. The technique provides a direct method for incorporating external environmental forces into the model. The model developed in this paper includes four major hydrodynamic forces: added mass, profile drag, fluid acceleration, and buoyancy. The model derived is a closed form solution which can be utilized in modern model-based control schemes.     

Functional perspective-based probabilistic fault detection and diagnostic algorithm for autonomous vehicle using longitudinal kinematic model

Microsystem Technologies - This paper describes a functional perspective-based probabilistic fault detection and diagnostic algorithm of an autonomous vehicle using a longitudinal kinematic model....     

A charging-scheme decision model for electric vehicle battery swapping station using varied population evolutionary algorithms

This paper proposes a new battery swapping station (BSS) model to determine the optimized charging scheme for each incoming Electric Vehicle (EV) battery. The objective is to maximize the BSS’s battery stock level and minimize the average charging damage with the use of different types of chargers. An integrated objective function is defined for the multi-objective optimization problem. The genetic algorithm (GA), differential evolution (DE) algorithm and three versions of particle swarm optimization (PSO) algorithms have been implemented to solve the problem, and the results show that GA and DE perform better than the PSO algorithms, but the computational time of GA and DE are longer than using PSO. Hence, the varied population genetic algorithm (VPGA) and varied population differential evolution (VPDE) algorithm are proposed to determine the optimal solution and reduce the computational time of typical evolutionary algorithms. The simulation results show that the performances of the proposed algorithms are comparable with the typical GA and DE, but the computational times of the VPGA and VPDE are significantly shorter. A 24-h simulation study is carried out to examine the feasibility of the model.     

标准模型下可证安全的基于身份门限环签名

在门限环签名中,任意n个成员组中的t个成员可以代表整个成员组产生(t,n)门限环签名,而对实际的签名者却具有匿名性。目前,基于身份的门限环签名方案大都是在随机预言模型下对其安全性进行证明的,然而在随机预言模型下可证安全的方案却未必是安全的,因此设计标准模型下的门限环签名方案更有意义。利用双线性对技术,提出了一种安全、高效的基于身份门限环签名方案,并在标准模型下基于计算Diffie-Hellman难问题证明方案满足适应性选择消息和身份攻击下的存在不可伪造性;同时,也对方案的无条件匿名性进行了证明。     

Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model

Today’s growth in the level of traffic in cities is leading to both congestion and environmental pollution (exhaust emissions and noise), as well as increased costs. Traffic congestion makes cities less pleasant places to live in, a particular problem being the negative impact on health as a result of increased exhaust emissions. In addition to these emissions, another major effect of transport which can lead to serious health problems is noise (EEA, 2013a, 2013b). There is a strong tendency in the world towards the development of “clean” motor vehicles that do not pollute the environment, that is, that do not emit harmful substances in their exhaust fumes and which create less noise without causing other types of pollution. The growth in the influence of transport on the environment has resulted in planners formulating procedures which take into account the effect of traffic on the quality of life in urban areas. This paper presents a model for the routing of light delivery vehicles by logistics operators. The model presented takes into account the fact that logistics operators have a limited number of environmentally friendly vehicles (EFV) available to them. When defining a route, EFV vehicles and environmentally unfriendly vehicles (EUV) are considered separately. For solving the problem of routing in the model, an adaptive neural network was used which was trained by a simulated annealing algorithm. An adaptive neural network was used for assessing the performance of the network branches. The input parameters of the neural network were the logistics operating costs and environmental parameters (exhaust emissions and noise) for the given vehicle route. Each of the input parameters of the neural network was thoroughly examined. The input parameters were broken down into elements which further describe the state of the environment, noise and logistics operating costs. After obtaining the performance of the network links for calculating the route for EFV and EUV vehicles a modified Clark–Wright algorithm was used. The proposed model was tested on a network which simulates the conditions in the very centre of Belgrade. All of the input parameters of the model were obtained on the basis of 40 automatic measuring stations for monitoring the air quality (SEA, 2012).     

Detecting the wheel pattern of a vehicle using stereo images

This paper presents a method for detecting the wheels of a vehicle in stereo image pairs. The method consists of two steps: (i) geometrical transformation; and (ii) circle extraction. The geometrical transformation uses the disparity values obtained from a stereo image pair to calculate the parameters of the plane containing wheels of the vehicle. By using these parameters, we transform any elliptical wheels contained in the plane to circular ones which can be extracted by the circle extraction algorithm. The circle extraction algorithm consists of (1) template matching and (2) Hough transform. In order to save computation and improve the results in the Hough transform, we employ two constraints (a) the neighbor-region edge connectivity and (b) the gradient direction of each edge point, to eliminate non-circular edge points. Experimental results show that these two constraints do eliminate non-circular edge points and preserve any circle embedded in edges. From the final results, we can observe that our method can detect and locate the wheels of a vehicle successfully.     

Small vehicle classification in the wild using generative adversarial network

Neural Computing and Applications - With the popularization of intelligent transportation system, the demand for vision-based algorithms and performance becomes more and severe. Vehicle detection...     

A launch vehicle stability analysis using the parameter plane method

An attitude control system, which uses the acceleration vector attitude as the control variable, has been postulated for control of the first stage of a large space booster. The goal of this system is to minimize lateral drift. A stability analysis of this system based upon the parameter plane method is described.     

A branch-and-price-and-check model for the vehicle routing problem with location congestion

This paper considers a vehicle routing problem with pickup and delivery, time windows and location congestion. Locations provide a number of cumulative resources that are utilized by vehicles either during service (e.g., forklifts) or for the entirety of their visit (e.g., parking bays). Locations can become congested if insufficient resources are available, upon which vehicles must wait until a resource becomes available before proceeding. The problem is challenging from a computational standpoint since it incorporates the vehicle routing problem and the resource-constrained project scheduling problem. The main contribution of this paper is a branch-and-price-and-check model that uses a branch-and-price algorithm that solves the underlying vehicle routing problem, and a constraint programming subproblem that checks the feasibility of the location resource constraints, and then adds combinatorial nogood cuts to the master problem if the resource constraints are violated. Experimental results show the benefits of the branch-and-price-and-check approach.