基于车-车通信的车辆车速动态控制系统设计

为了更好地解决车辆纵向追尾碰撞问题,利用车联网技术,设计了基于车-车通信的车辆车速动态控制系统,在系统软硬件设计的基础上,实现了车辆之间的车速、位置等关键数据信息的相互共享,并且通过车载APP显示。系统通过对自车与前车之间的实际车距和理论安全距离比较,控制电机实现对本车的车速动态控制。实验结果表明,该系统能够有效避免车辆纵向追尾碰撞风险。     

基于道路坡度实时信息的经济车速优化方法

作为生态驾驶辅助系统的组成部分,提出了基于道路坡度实时信息的车辆经济车速优化方法。搭建了车辆油耗模型和车辆动力学模型,对于车辆行驶在没有坡度的和前方出现坡道的两种情况,进行最省油车速优化。根据坡度信息,利用动态规划算法,计算出通过坡道的经济车速。对于一款D级轿车,运用Matlab/Simulink和Carsim联合仿真,与真实道路信息上实车模拟结果进行对比分析。结果表明:与传统定速巡航控制算法相比,该算法节油达到5%。这说明,该算法能降低车辆油耗。结合全球定位系统(GPS)信息以及地理信息系统(GIS),该技术可用于基于车联网的生态驾驶辅助系统。     

基于中心差分卡尔曼滤波的车速估计研究

针对车辆行驶过程中车速估计问题,论文基于中心差分卡尔曼滤波理论设计了车速的估计算法。建立了非线性三自由度车辆估算模型,根据纵向加速度、侧向加速度和方向盘转角低成本传感器信号的信息融合,实现对车速的准确估计,应用CarSim与Matl ab/Simulink联合仿真实验对算法进行验证。结果表明:估计算法能够准确估计车辆行驶过程中的纵向车速、侧向车速和质心侧偏角。     

基于目标速度的汽车ACC系统油门控制策略研究

ACC系统能够根据雷达等传感器检测到的前方车辆行驶信息,并自动控制本车的油门开度和制动强度,实现自适应巡航行驶,通过对车辆行驶纵向阻力特性的分析,针对目前广泛使用的基于目标加速度的油门开度控制策略受车辆装载质量影响较大的情况,利用功率平衡原理,提出了1种基于目标车速的油门开度控制策略,并利用PreScan软件对基于目标车速的油门开度控制策略进行了仿真实验,仿真结果表明了该控制策略有效的避免了整车装载质量变化对控制目标的影响。     

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。     

基于卡尔曼滤波的汽车纵向速度滤波算法及应用

针对现有车速计算方法精度不高、波动大的问题,研究基于车速信号的车辆纵向速度卡尔曼滤波估计算法,并实现该算法在整车控制器中的应用。仿真和实车试验表明,该卡尔曼滤波车速算法具有良好的实时性与较高的滤波精度。     

基于多传感器信息融合的汽车行驶状态估计

为了保证汽车的主动安全控制,需要准确地估计车辆行驶状态信息。针对目前汽车状态估计中由于技术条件限制和成本过高造成的部分参数无法测量或不易测量的问题,本文中利用低成本传感器,基于信息融合技术进行汽车行驶状态估计。建立了包括横摆、横向和纵向的3自由度非线性汽车动力学模型,同时为降低噪声对系统影响,建立了自适应无迹卡尔曼滤波(AUKF)的信息融合算法,给出车辆状态最小方差意义下的融合结果。利用纵向加速度、侧向加速度和转向盘转角等低成本传感器信号融合得到所需的难以测量的质心侧偏角、横摆角速度和纵向车速。通过Matlab/Simulink-CarSim联合仿真和实车试验对所研究的估计算法进行了试验验证。试验结果表明:该算法能够准确地估计汽车质心侧偏角、横摆角速度和纵向车速,且相比于无迹卡尔曼滤波(UKF),本算法提高了估计精度和实时性。     

基于长下坡路段车速与制动器温度实时监测的模糊推理预警算法

提出一种实时的车辆长下坡路段车速与制动器温度预警算法.建立长下坡路段的整车纵向力平衡方程和能量方程,分析制动器耗散能量占总能量的比例,研究车速对制动器耗散能量大小的影响,结合制动器吸收能量占制动器耗散能量的比例经验公式,建立制动器温升计算模型;基于试验数据,采用最小二乘法确定模型中的待定系数,比较模型计算的温升与试验数据,最大的均方根误差为12.1℃,对应车速为38 km/h,最小均方根误差为3.7℃,对应车速为50 km/h.安全车速根据安全制动距离和路面纵坡计算得出.预警算法依据车速和制动器温度变化,构造模糊推理系统计算车辆危险指数,综合评价车辆下长坡的危险程度.     

基于VCU的商用车车速信号处理技术

商用车驾驶过程中,车速是仪表向驾驶员提供信息中较为重要的信息,车速指示的准确性、可靠性、响应性直接影响车辆运行的安全和驾驶体验,以及巡航等控制功能的品质。本文介绍一种基于VCU (整车控制器,Vehicle Control Unit)的车速处理方法 , VCU同时采集车速传感器脉冲信号和接受CAN总线的ABS轮速信号,经过综合计算处理后输出车速至CAN总线供仪表显示及其它控制功能使用,试验表明此种方法处理的车速具有分辨率高、误差小、可靠性高、后期市场维护便捷等优点。     

考虑道路因素的智能车辆纵向运动决策与控制研究

针对智能车辆纵向运动时的交通道路适应性问题,考虑路面附着系数和前车运动速度等因素,研究了智能车辆纵向运动决策与控制方法。论文研究了基于车头时距的纵向运动决策方法并建立不同驾驶行为的目标车速模型,运用变论域模糊推理算法设计了目标加速度模型。基于纵向动力学模型,运用自适应反演滑模控制算法建立了驱动控制器和制动控制器。对高附着系数路面和低附着系数路面的行驶工况进行仿真试验验证,结果表明,在不同的附着系数路面和前车变速行驶条件下,智能车辆能实时、合理地决策目标车速、目标加速度,实现安全、高效、稳定的跟驰。     

Vehicle longitudinal velocity estimation during the braking process using unknown input Kalman filter

In this paper, vehicle longitudinal velocity during the braking process is estimated by measuring the wheels speed. Here, a new algorithm based on the unknown input Kalman filter is developed to estimate the vehicle longitudinal velocity with a minimum mean square error and without using the value of braking torque in the estimation procedure. The stability and convergence of the filter are analysed and proved. Effectiveness of the method is shown by designing a real experiment and comparing the estimation result with actual longitudinal velocity computing from a three-axis accelerometer output.     

Sideslip angle estimation considering short-duration longitudinal velocity variation

The accurate estimation of sideslip angle is necessary for many vehicle control systems. The detection of sliding and skidding is especially critical in emergency situations. In this paper, a sideslip angle estimation method is proposed that considers severe longitudinal velocity variation over the short period of time during which a vehicle may lose stability due to sliding or spinning. An extended Kalman filter (EKF) based on a kinematic model of a vehicle is used without initialization of the inertial measurement unit to estimate vehicle longitudinal velocity. A dynamic compensation method that compensates for the difference in the locations of the vehicle velocity sensor and the IMU in on-road vehicle tests is proposed. Evaluations with a CarSim™ 27-degree-of-freedom (DOF) model for various vehicle test scenarios and with on-road tests using a real vehicle show that the proposed sideslip angle estimation method can accurately predict sideslip angle, even when vehicle longitudinal velocity changes significantly.     

Drive control system design for stability and maneuverability of a 6WD/6WS vehicle

This paper describes a drive controller designed to improve the lateral vehicle stability and maneuverability of a 6-wheel drive / 6-wheel steering (6WD/6WS) vehicle. The drive controller consists of upper and lower level controllers. The upper level controller is based on sliding control theory and determines both front and middle steering angle, additional net yaw moment, and longitudinal net force according to the reference velocity and steering angle of a manual drive, remotely controlled, autonomous controller. The lower level controller takes the desired longitudinal net force, yaw moment, and tire force information as inputs and determines the additional front steering angle and distributed longitudinal tire force on each wheel. This controller is based on optimal distribution control and takes into consideration the friction circle related to the vertical tire force and friction coefficient acting on the road and tire. Distributed longitudinal/lateral tire forces are determined as proportion to the size of the friction circle according to changes in driving conditions. The response of the 6WD/6WS vehicle implemented with this drive controller has been evaluated via computer simulations conducted using the Matlab/Simulink dynamic model. Computer simulations of an open loop under turning conditions and a closed-loop driver model subjected to double lane change have been conducted to demonstrate the improved performance of the proposed drive controller over that of a conventional DYC.     

Fuzzy Approach to the Real Time Longitudinal Velocity Estimation of a FWD Car in Critical Situations

This paper presents a new approach to the fuzzy estimation of the variables of complex, fast, closed-loop systems. It is used to develop an original real-time longitudinal velocity estimator for FWD cars. Its application covers highly critical driving situations and avoids the use of an expensive optical cross-correlation sensor. The aim is to provide vehicle monitoring processes with a reliable value of the longitudinal velocity. Fuzzy aggregate indicators are used to identify and detect the different ways a vehicle behaves. Then, a fuzzy expert system with rules based on these indicators decides which values should be used among those which allow the estimation of the longitudinal velocity.     

Development of a path planning system using mean shift algorithm for driver assistance

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.     

Non-singular slip (NSS) method for longitudinal tire force calculations in a sudden braking simulation

In a dynamic vehicle simulation, longitudinal tire force is primarily based on the longitudinal slip (ratio). In the longitudinal slip formula, state variables are used in the denominator. This causes a divergence problem for numerical simulations of vehicle dynamics. To avoid this numerical singularity, a differential slip calculation method was developed for use in dynamic vehicle simulations. However, this method also causes a singularity when the wheel velocity approaches zero in a pure slip state, such as during sudden braking. In this paper, a new longitudinal slip calculation method, which can overcome singularities in all velocity conditions, is proposed. For this purpose, the Taylor series is adapted to the slip formula and the idea of virtual wheel rotation stiffness is introduced for the development of the slip equation. The physical phenomenon at the zero slip state is analyzed. Finally, the proposed slip formula is used to solve the numerical singularity problem, and the non-singular slip (NSS) calculation method is proposed. The proposed NSS method is applied to tire model performance test (TMPT) simulations to validate its performance.     

Fuzzy Approach to the Real Time Longitudinal Velocity Estimation of a FWD Car in Critical Situations

SUMMARY

This paper presents a new approach to the fuzzy estimation of the variables of complex, fast, closed-loop systems. It is used to develop an original real-time longitudinal velocity estimator for FWD cars. Its application covers highly critical driving situations and avoids the use of an expensive optical cross-correlation sensor. The aim is to provide vehicle monitoring processes with a reliable value of the longitudinal velocity. Fuzzy aggregate indicators are used to identify and detect the different ways a vehicle behaves. Then, a fuzzy expert system with rules based on these indicators decides which values should be used among those which allow the estimation of the longitudinal velocity.     

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。     

无人驾驶车辆解耦混合运动规划方法研究

高速公路无人驾驶车辆运动规划要充分考虑车辆的动力学特性和道路环境构成的复杂约束,优先确保车辆的无碰撞行驶轨迹,同时算法实时性比常速情况下要求更高,要充分考虑计算的复杂性。提出一种可生成横纵向最优解逼近群的解耦增强混合运动规划方法,引入Frent坐标系将运动规划问题进行横纵向解耦,在横向偏移规划中融合数值优化和多项式规划算法,在纵向速度规划中融合围绕速度和围绕避障规划算法,解耦规划完成后使用代价函数和碰撞检测方法对生成的轨迹群进行评估和选择。搭建Apollo-LGsvl无人驾驶联合仿真测试平台,对所提出的运动规划方法进行仿真验证。结果表明,提出的横纵向解耦增强混合运动规划算法在高速公路各工况中所生成的运动轨迹能够有效避免车辆发生碰撞,其横向偏移和纵向速度均能满足二阶平滑,最大横向加速度、最大纵向加速度、最大纵向jerk等满足动力学约束。该方法有效减少了最优解逼近群内的轨迹数量,使轨迹评估阶段计算复杂度降低了46%,证明提出的方法具备一定的应用价值。     

Robust control of anti-lock brake system

A robust control algorithm for an anti-lock brake system is proposed. The method used is based on static-state feedback of longitudinal slip and does not involve controller scheduling with changing vehicle speed or road adhesion coefficient estimation. An improvement involving scheduling of longitudinal slip reference with longitudinal acceleration measurement is included. Electromechanical braking actuators are used in simulations, and the algorithm used in this study is shown to have high performance on roads with constant and varying adhesion coefficients, displaying nice robustness properties against large vehicle speed and road adhesion coefficient variations. Guidelines are provided for tuning controller gains to cope with unknown actuator delay and measurement noise.