Analyzing human driving data an approach motivated by data science methods

By analyzing a large data-base of car-driving data in a generic way, a few elementary facts on car-following have been found out. The inferences stem from the application of the mutual information to detect correlations to the data. Arguably, the most interesting fact is that the acceleration of the following vehicle depends mostly on the speed-difference to the lead vehicle. This seems to be a causal relationship, since acceleration follows speed-difference with an average delay of 0.5 s. Furthermore, the car-following process organizes itself in such a manner that there is a strong relation between speed and distance to the vehicle in front. In most cases, this is the dominant relationship in car-following. Additionally, acceleration depends only weakly on distance, which may be surprising and is at odds to a number of simple models that state an exclusive dependency between acceleration and distance.     

基于跟车思想的一维元胞自动机交通流模型

提出一个改进的一维元胞自动机模型来模拟周期性边界条件下高速公路上车流运动.基于跟车模型的思想,根据所研究车辆与其前方紧邻车辆之间的间距和相对速度来确定该车的运动,间接地反映了次近邻车辆的影响.通过引入安全间距来描述高速运动车辆接近前方缓行车辆时的减速行为,并利用随机减速概率来反映减速行为中的随机因素.由于安全间距的引入,当减速概率大于零时在较高密度下就出现完全的阻塞相.同时在本模型中采用的是有条件减速,因而可以较好描述交通实测中观察到的现象.在临界密度附近,车流运动处于亚稳态并呈现出滞后现象.由于本模型对于车辆微观运动的合理描述,可以直接用以研究在交通灯控制下城市道路交通中的各种现象.     

Full velocity difference and acceleration model for a car-following theory

In order to describe the car-following behavior more actually in real traffic, a full velocity difference and acceleration model (for short, FVDAM) is proposed by synthetically taking into account headway, velocity difference and acceleration of the leading car on the basis of full velocity difference model. The analytical method and numerical simulation results show that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, that incorporating the acceleration of the leading car into car-following model can stabilize traffic flow, suppress the traffic jam and increase capacity, and that the following car in FVDAM can accelerate more quickly than in FVDM.     

Modeling the asymmetry in traffic flow (a): Microscopic approach

The asymmetric characteristic of a vehicle’s ability in deceleration and acceleration, as well as its impact to micro- and macroscopic traffic flow has caused increased attention from both theoretical and practical sides. However, how to realistically model this property remains a challenge to researchers. This paper is one of the two studies on this topic, which is focused on the modeling at the microscopic level from the investigation of car-following behavior. The second part of the study [H. Liu, H. Xu, H. Gong, Modeling the asymmetry in traffic flow (b): macroscopic approach, Appl. Math. Model. (submitted for publication)] is focused on the modeling of this asymmetric property from the macroscopic scale. In this paper, we first present an asymmetric full velocity difference car-following approach, in which a higher order differential equation is developed to take into account the effect of asymmetric acceleration and deceleration in car-following. Then, efforts are dedicated to calibrate the sensitivity coefficients from field data to complete the theoretical approach. Using the data recorded from the main lane traffic and ramp traffic of a segment of the US101 freeway, the two sensitivity coefficients have been successfully calibrated from both congested and light traffic environments. The experimental study reveals that in the studied traffic flow, the intensity of positive velocity difference term is significantly higher than the negative velocity difference term, which agrees well with the results from studies on vehicle mechanics.     

Invariance and homogenization of an adaptive time gap car-following model

In this paper we consider a microscopic model of traffic flow called the adaptive time gap car-following model. This is a system of ODEs which describes the interactions between cars moving on a single line. The time gap is the time that a car needs to reach the position of the car in front of it (if the car in front of it would not move and if the moving car would not change its velocity). In this model, both the velocity of the car and the time gap satisfy an ODE. We study this model and show that under certain assumptions, there is an invariant set for which the dynamics is defined for all times and for which we have a comparison principle. As a consequence, we show rigorously that after rescaling, this microscopic model converges to a macroscopic model that can be identified as the classical LWR model for traffic.     

遗传算法在自动泊车辅助系统模糊控制中的应用

随着汽车工业的发展,自动泊车辅助系统已逐渐成为汽车的必备装置.对自动泊车控制过程进行了分析,设计了自动泊车辅助系统模糊控制器,并将遗传算法应用于模糊控制器参数寻优过程,较为有效的确定了模糊控制器的参数,使用遗传算法工具箱对模糊控制器的隶属度函数进行了优化.并在Matlab环境下,对自动泊车模糊控制进行了仿真研究,论述了遗传算法在改善模糊控制效果中的应用.     

磁浮列车的模糊反馈控制

为了进一步提高了列车的承载能力，本文利用电流互感器、间隙传感器和加速度表作为敏感元件，对悬浮系统进行反馈控制，并根据悬浮质量的不同，对反馈参数进行模糊调整。分析的结果表明，这种设计的模糊反馈控制器可以提高磁浮列车的承载能力。     

Stability of vehicles under nonstationary crosswind excitation

Strong crosswind gusts have great influence on the stability of railway and road vehicles. They may lead to accidents and also to a discomfort for the road vehicle driver. Risk assessment for overturning of railway and road vehicles is usually calculated based on the stationary situation or at least on wind-tunnel experiments that are mostly carried out with a static vehicle model. Nonstationary excitation due to wind turbulence occurs if the vehicle accelerates or decelerates. Increasing vehicle speed relative to wind speed will move the energy content of the spectrum to a higher frequency range. It has been realized that nonstationary wind has a great influence on vehicle stability especially when the vehicle speed is high. Thus in order to assess the overturning risk in a more realistic way, a nonstationary wind model together with its interaction with the vehicle should be taken into consideration.This paper proposes a nonstationary wind turbulence model for the investigation of crosswind stability of ground vehicles. A wind model with nonstationary turbulence as well as the wind effects to the moving vehicle in a nonstationary situation (acceleration/deceleration) is described. Nonstationary aerodynamic forces are considered together with the interaction between the moving vehicle system and the wind turbulence. Failure probabilities are computed and reliability analyses are carried out. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Fuzzy controller of the air system of a diesel engine: Real-time simulation

In this paper a fuzzy controller is proposed to regulate the intake manifold pressure and the fresh mass airflow of diesel engines simultaneously. The instrumentation set usually embedded in a mass-produced passenger car has been considered. Unlike many multi-variable controllers, the proposed structure requires neither an internal model nor identification algorithms. In comparison to controllers embedded at present in standard engine control units (ECUs), it improves the trajectory tracking of desired outputs during simulation of EURO cycles. Because of its performance, the fuzzy controller has been implemented in an electronics control unit. Some real-time results are presented.     

Numerical and experimental investigation on stochastic dynamic load of a heavy duty vehicle

Numerical simulation and field test are used to investigate tire dynamic load. Based on multi-body dynamics theory, a nonlinear virtual prototype model of heavy duty vehicle (DFL1250A9) is modeled. The geometric structural parameters of the vehicle system, the nonlinear characteristics of shock absorber and leaf springs are precisely described. The dynamic model is validated by testing the data, including vertical acceleration of driver seat, front wheel, intermediate wheel and rear wheel axle head. The agreement between the response of the virtual vehicle model and the measurements on the test vehicle is satisfactory. Using the reliable model, the effects of vehicle speed, load, road surface roughness and tire stiffness on tire dynamic load and dynamic load coefficient (DLC) are discussed. The results demonstrate that the proposed model can offer efficient and realistic simulation for stochastic dynamic loads, so as to investigate vehicle road-friendliness.     

Addendum to the paper “on certain exact solutions of the fourier equation for regions varying with time” : PMM vol. 37, n6, 1973, pp. 1145–1146

The exact solutions given in [1] are generalized to the case of cylindrical and spherical sectors rotating about the azimuth relative to the coordinate origin either at a uniform rate or with uniform acceleration (or deceleration). The class of equations of motion of the boundaries of the half-space (in the Cartesian coordinates) which lead to exact solutions of the Fourier equation defined in these domains, is enlarged.     

A modified coupled map car following model and its traffic congestion analysis

Based on the coupled map car following model which was presented by Konishi et al. [Konishi KJ, Kokame H, Hirate K. Phys Rev E 1999;60:4000-7.] (for short, KKH), a modified coupled map car following model is proposed. In this model two successive vehicles’ headway distances in front of the considered one are incorporated in the optimal velocity (for short, OV) function. The stability condition is given for the change of the speed of the preceding vehicle on the base of the control theory. The control scheme in KKH model is applied to the modified model and the feedback gains are determined. Comparison between the modified model and KKH model is carried out. And the corresponding numerical simulation results show that the temporal behavior obtained by our model is better than that by KKH model. The simulation results are in good agreement with the theoretical analysis.     

A self-organizing adaptive fuzzy controller

There are two main parts in this paper. The first part presents a knowledge representation and reasoning scheme, called tree-searched neural networks (TNN). The TNN is based on a well-known intuitive knowledge representation (IKR) and can reduce the number of the processing nodes in the neural networks. The second part proposes a self-organizing adaptive fuzzy controller (SOAFC) based on the TNN model. It can help acquire control knowledge and thus can reduce the dependence on experts. Furthermore, designers do not need to predefine all membership functions to cover whole input space domain. For improving its performance further, we design a D-controller which is included within the SOAFC. Whether the fuzzy controller is incorporated with the D-controller or not, it is also guaranteed to be globally stable. Simulation results show that this approach has faster convergence speed, results in better transient response, and in addition requires less total control energy.     

基于模糊传感器的机器人动态障碍环境中的运动控制

对自主式机械手在动态和部分已知且存在运动障碍环境中的运动规划和控制进行了研究,解决了自由碰撞运动控制中具有普遍意义的问题。利用人工势能场的机器人导航控制技术由模糊控制实现,系统的稳定性由李雅普诺夫原理保证。模糊控制器为机器人伺服提供控制指令,使机器人在不可预知的环境中能实时地、自主地选择到达目标的路径和方向。在动态环境的实时控制中,基于传感器的运动控制是处理未知模型和障碍物的重要控制方式。     

Location with acceleration–deceleration distance

In this paper we investigate a model where travel time is not necessarily proportional to the distance. Every trip starts at speed zero, then the vehicle accelerates to a cruising speed, stays at the cruising speed for a portion of the trip and then decelerates back to a speed of zero. We define a time equivalent distance which is equal to the travel time multiplied by the cruising speed. This time equivalent distance is referred to as the acceleration–deceleration (A–D) distance. We prove that every demand point is a local minimum for the Weber problem defined by travel time rather than distance. We propose a heuristic approach employing the generalized Weiszfeld algorithm and an optimal approach applying the Big Triangle Small Triangle global optimization method. These two approaches are very efficient and problems of 10,000 demand points are solved in about 0.015 seconds by the generalized Weiszfeld algorithm and in about 1 minute by the BTST technique. When the generalized Weiszfeld algorithm was repeated 1000 times, the optimal solution was found at least once for all test problems.     

A neural fuzzy control system with structure and parameter learning

A general connectionist model, called neural fuzzy control network (NFCN), is proposed for the realization of a fuzzy logic control system. The proposed NFCN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The NFCN can be constructed from supervised training examples by machine learning techniques, and the connectionist structure can be trained to develop fuzzy logic rules and find membership functions. Associated with the NFCN is a two-phase hybrid learning algorithm which utilizes unsupervised learning schemes for structure learning and the backpropagation learning scheme for parameter learning. By combining both unsupervised and supervised learning schemes, the learning speed converges much faster than the original backpropagation algorithm. The two-phase hybrid learning algorithm requires exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a reinforcement neural fuzzy control network (RNFCN) is further proposed. The RNFCN is constructed by integrating two NFCNs, one functioning as a fuzzy predictor and the other as a fuzzy controller. By combining a proposed on-line supervised structure-parameter learning technique, the temporal difference prediction method, and the stochastic exploratory algorithm, a reinforcement learning algorithm is proposed, which can construct a RNFCN automatically and dynamically through a reward-penalty signal (i.e., “good” or “bad” signal). Two examples are presented to illustrate the performance and applicability of the proposed models and learning algorithms.     

Curvature-Based Real-Time NURBS Surface Interpolator with Look-Ahead ACC/DEC Control

Real-time parametric surface interpolation is very useful in high-performance machining. A curvature-based NURBS surface interpolator with look-ahead acceleration/deceleration (ACC/DEC) control has been developed. The cutter contact (CC) paths are planned through iso-parametric line method. The CC feedrate profile is optimized using global flexible control strategy and local adaptive ACC/DEC optimization strategy according to the look-ahead algorithm. The real-time surface interpolation algorithm was programmed on Windows XP platform. The stability and efficiency of the proposed interpolation method were verified by a NURBS curve and a NURBS surface. It is shown that the proposed parametric interpolation algorithm can satisfy the high speed and high precision requirements of high-speed CNC systems.     

Design of a coordinated adaptive backstepping tracking control for nonlinear uncertain active suspension system

This paper presents a novel adaptive backstepping tracking control for nonlinear uncertain active suspension system, which can achieve the coordinated control over the sprung-mass acceleration and suspension dynamic displacement for nonlinear uncertain active suspension system based on a developing model-reference system. First, according to adaptive backstepping control principle, this model-reference system is designed with purpose of providing the ideal reference trajectories for the sprung-mass displacement and vertical velocity, respectively. Then, the design of a coordinated adaptive backstepping tracking controller is conducted to make the control plant accurately track the prescribed performances of the model-reference system by virtue of the backstepping technique and Lyapunov stability theory, in which a virtual controller with online parameter regulation rules is designed and implemented to guarantee the stability of vehicle body. Finally, a numerical example is provided to verify the effectiveness of our designed adaptive backstepping tracking controller under various operating scenarios.     

Stability analysis of sampled-data fuzzy controller for nonlinear systems based on switching T–S fuzzy model

This paper investigates the system stability of a sampled-data fuzzy-model-based control system, formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampled-data fuzzy controller has an advantage that it can be implemented using a microcontroller or a digital computer to lower the implementation cost and time. However, discontinuity introduced by the sampling activity complicates the system dynamics and makes the stability analysis difficult compared with the pure continuous-time fuzzy control systems. Moreover, the favourable property of the continuous-time fuzzy control systems which is able to relax the stability analysis result vanishes in the sampled-data fuzzy control systems. A Lyapunov-based approach is employed to derive the LMI-based stability conditions to guarantee the system stability. To facilitate the stability analysis, a switching fuzzy model consisting of some local fuzzy models is employed to represent the nonlinear plant to be controlled. The comparatively less strong nonlinearity of each local fuzzy model eases the satisfaction of the stability conditions. Furthermore, membership functions of both fuzzy model and sampled-data fuzzy controller are considered to alleviate the conservativeness of the stability analysis result. A simulation example is given to illustrate the merits of the proposed approach.     

Trajectory stabilization of a model car via fuzzy control

This paper deals with trajectory stabilization of a computer simulated model car via fuzzy control. Stability conditions of fuzzy systems are given in accordance with the definition of stability in the sense of Lyapunov. First, we approximate a computer simulated model car, whose dynamics is nonlinear, by T-S (Takagi and Sugeno) fuzzy model. Fuzzy control rules, which guarantee stability of the control system under a condition, are derived from the approximated fuzzy model. The simulation results show that the fuzzy control rules effectively realize trajectory stabilization of the model car along a given reference trajectory from all initial positions under a condition and the dynamics of the approximated fuzzy model agrees well with that of the model car.