汽车主动防撞系统的规避控制研究

提出了一种汽车主动防撞系统,介绍了该防撞系统的工作原理,并对其规避控制进行了研究。依据汽车运动学理论对安全车距模型进行了分析,建立了包括安全临界、锁定目标、危险临界和极限临界的汽车纵向安全车距模型以及横向安全车距模型;结合安全车距对危险目标进行识别和分类;针对不同危险程度的目标制定了相应的规避控制策略。     

G1501 跨泖港大桥主动防撞系统设计

G1501高速公路跨泖港大桥上跨平申线(上海段)航道。该航道是《上海市“十二五”内河高等级航道建设规划》中首批启动建设的航道,为黄浦江上游三大支流之一,目前航道等级Ⅴ改Ⅳ。改造过程中航道上桥梁被船撞风险高,通过对桥梁预防航道船舶碰撞预警系统工作模式与参数化技术、多源数据三维测量空间的平面转换算法与工程实现技术、基于多源数据融合的船舶通航异常行为的判别技术、桥梁预防航道船舶碰撞预警系统性能优化与工程测试技术等内容进行研究,突破基于多源数据融合的复杂背景下航道多目标检测/跟踪算法、基于多源数据融合的船舶-桥撞击态势预测等关键技术,泖港大桥采用主红外、可见光和激光测距三类传感器复合体制的航道桥梁主动防撞系统。从而实现全天候、全天时、全自动航道桥梁主动防撞监控及预警,其应用效果良好。     

现代汽车主动防撞综合安全技术的研究

介绍了现有汽车主动安全技术和被动安全技术,分析现有汽车安全技术的局限性,提出了一种综合主动安全技术与被动安全技术的主动作用型智能化的液压缓冲防撞安全技术。     

泰远汽车自动防撞器正式上市

<正>2014年7月19日,"汽车驾驶安全论坛暨泰远汽车自动防撞器全球上市新闻发布会"在重庆隆重召开。泰远汽车自动防撞器发明人刘泰远院长亲临现场,对泰远牌汽车自动防撞器的研发、性能、原理做了详细阐述。泰远牌汽车自动防撞器是集声、光、电、机于一体的高科技产品,当车探测到危险障碍物时,它能够自动报警、自动减速、自动制动,能有效地保护车辆和司乘人员的安全,是能够集汽车、人、防撞器系统为一体的智能主动安全的高科技技术,从根本上区别于被动型的安全带、安全气囊、安全玻璃等减害装置。在事故即将发生时,提前采取主动安全智能制动措施,最大限度地避免或减轻事故的发生,防     

超声波测距和汽车防撞雷达的设计

介绍一种基于单片机控制的超声波测距系统的软硬件构成、工作原理和误差分析。利用本系统及其设计方法可以开发汽车防撞雷达。     

分布式大型车辆防撞预警系统设计与实现

大型车辆的交通事故已经成为社会关注的热点问题,减少碰撞风险是交通安全研究的重要内容。根据大型车辆前向碰撞预警、后方碰撞预警和侧向防撞预警的要求,设计了一种全方位防撞预警系统。系统以分布式信息采集、处理和报警平台为基础,通过基于安全距离的算法实现分级预警。实车预警实验表明该系统能够根据安全等级准确地给出报警信息。     

基于机器学习的防撞梁低速碰撞性能预测研究

文章根据GB 17354-1998法规的试验工况,提出了一种基于机器学习的汽车防撞梁低速碰撞性能预测方法。通过将防撞梁的几何结构进行参数化表征,构建防撞梁几何结构数据集;结合几何参数和布置参数,构建简化的摆锤冲击工况,进而形成机器学习训练数据集。以摆锤对防撞梁的侵入量作为性能指标,采用三种预测模型进行侵入量的预测效果验证。结果表明,支持向量机模型和多层感知器模型表现出较高的预测精度,并在实际工况预测中取得了较好的结果。该方法可在整车开发中实施应用,能够大幅缩短防撞梁摆锤冲击性能的评估周期,同时可为几何结构的寻优提供帮助指导。     

基于OpenCV的AEB系统车辆检测和预警研究

为降低汽车换道时碰撞事故发生概率,提出基于OpenO_4CV的AEB系统车辆检测和预警算法。首先利用Haar-like+Adaboost实现前方车辆的识别与检测,并结合粒子滤波原理建立车辆跟踪模型。然后基于单目视觉模型对前方车辆距离进行测量,根据障碍物与车辆的安全距离估测碰撞时间。最后,基于AEB系统进行车辆防撞预警测试,测试仿真结果表明,在不干扰驾驶员正常驾驶的前提下,即碰时间的TTC算法性能最佳,有效提升了前方车辆检测预警精确率。     

基于最小模型误差估计的智能汽车路径跟踪控制

为提高分布式驱动电动智能汽车在自主循迹过程中关键参数的估计精度并降低模型不确定性对控制系统鲁棒性的影响,本文中提出了一种基于观测器的自适应滑模路径跟踪控制策略。首先,针对难以直接精确测量的车辆纵、侧向速度,建立了5输入3输出3状态的状态估计系统,并采用最小模型误差准则以降低估计过程轮胎的非线性特性带来的观测模型误差。接着,基于运动学模型,计算出了路径跟踪期望横摆角速度响应,并采用自适应滑模算法实现主动转向控制。考虑线控转向系统的潜在失效风险,引入径向基神经网络对系统不确定性进行在线估计。同时,设计了直接横摆稳定控制器并采用最优转矩分配策略,进一步提高车辆的稳定性。最后,对车辆状态估计和路径跟踪进行了Carsim/Matlab联合仿真,结果表明:基于最小模型误差准则的观测器能取得较可靠的估计结果,路径跟踪控制器能保证车辆具有较好的跟踪精度和鲁棒性。     

滑动式防撞护栏在道路安全防护中的理论及其工程应用

针对我国公路交通运输中车辆的安全事故,尤其是车辆冲出路面的安全事故,提出采用基于"宽容型"设计理念的滑动式防撞护栏,通过对滑动式防撞护栏的防撞机理和整体结构研究并对防撞性能进行验算和结果分析,确定了施工工艺及质量控制要点。并对实际工程防护效果进行了跟踪和验证。     

融合预瞄与势场栅格法的紧急避撞驾驶人模型

紧急避障工况下的驾驶人操作具有响应快且动作幅值较大的特点，传统预瞄驾驶人模型已不能适应紧急避障工况的需求，故考虑实际避撞场景开发相应的驾驶人模型就显得尤为必要。针对此种状况，基于驾驶模拟器，结合紧急避撞工况实际驾驶人操纵数据，提出了一种融合预瞄与势场栅格法的紧急避撞驾驶人模型。首先针对紧急避撞工况下车辆运动特点，建立车辆横、纵向耦合非线性动力学模型，并给出其状态空间方程描述；其次，离线仿真分析紧急避撞系统特征，并结合线性二次型最优控制，建立最优曲率预瞄+跟踪误差反馈驾驶人模型；再者，基于紧急避撞工况下真实驾驶人经验转向行为数据，开发基于势场栅格法的驾驶人模型，为进一步提高驾驶人模型对避障行驶工况的适应性，将基于势场栅格法的驾驶人模型与最优曲率预瞄+跟踪误差反馈驾驶人模型进行融合，并基于Sigmoid函数实现两者输出的权重分配；最后，针对所提出的融合预瞄与势场栅格法的驾驶人模型，开展基于避撞台架的驾驶人在环仿真试验以及实车试验。研究结果表明：在紧急避撞工况下，对比最优曲率预瞄+跟踪误差反馈驾驶人模型，融合预瞄与势场栅格法的驾驶人模型输出的转向动作与实际驾驶人行为较为接近，可在保证避障安全性的前提下，兼顾避障路径跟踪精度与车辆行驶的稳定性。     

Optimal control of brakes and steering for autonomous collision avoidance using modified Hamiltonian algorithm

ABSTRACT

This paper considers the problem of collision avoidance for road vehicles, operating at the limits of friction. A two-level modelling and control methodology is proposed, with the upper level using a friction-limited particle model for motion planning, and the lower level using a nonlinear 3DOF model for optimal control allocation. Motion planning adopts a two-phase approach: the first phase is to avoid the obstacle, the second is to recover lane keeping with minimal additional lateral deviation. This methodology differs from the more standard approach of path-planning/path-following, as there is no explicit path reference used; the control reference is a target acceleration vector which simultaneously induces changes in direction and speed. The lower level control distributes vehicle targets to the brake and steer actuators via a new and efficient method, the Modified Hamiltonian Algorithm (MHA). MHA balances CG acceleration targets with yaw moment tracking to preserve lateral stability. A nonlinear 7DOF two-track vehicle model confirms the overall validity of this novel methodology for collision avoidance.     

Development of a rear-end collision avoidance system with automatic brake control

We have studied active safety technologies from the standpoint of “collision avoidance”. This paper describes a rear-end collision avoidance system with automatic brake control, which avoids a collision to the vehicle in front caused by inadvertent human errors using automatic emergency braking. The system is comprised of four key technological elements, headway distance measurement using scanning laser radar, path estimation algorithm with vehicle dynamics, collision prediction to the vehicle in front by a safe/danger decision algorithm, and longitudinal automatic brake control.     

Evaluation of automotive forward collision warning and collision avoidance algorithms

Collision warning/collision avoidance (CW/CA) systems target a major crash type and their development is a major thrust of the Intelligent Vehicle Initiative. They are a natural extension of adaptive cruise control systems already available on many car models. Many CW/CA algorithms have recently been proposed but the existing literature mainly focuses on algorithm development. Evaluations of these algorithms have been usually based on subjective ratings. The main contribution of this paper is the utilization of a naturalistic driving data set for the evaluation of CW/CA algorithms. We first collect manual driving data from the ICCFOT project, then process the data by Kalman smoothing, and finally identify 'threatening' and 'safe' data sets according to vehicle brake inputs and vehicle range behavior. Five CW/CA algorithms published in the literature are evaluated against the identified data sets. The performance of these algorithms is determined through a performance metric commonly used in signal detection and information retrieval under unbalanced data population.     

Evaluation of automotive forward collision warning and collision avoidance algorithms

Collision warning/collision avoidance (CW/CA) systems target a major crash type and their development is a major thrust of the Intelligent Vehicle Initiative. They are a natural extension of adaptive cruise control systems already available on many car models. Many CW/CA algorithms have recently been proposed but the existing literature mainly focuses on algorithm development. Evaluations of these algorithms have been usually based on subjective ratings. The main contribution of this paper is the utilization of a naturalistic driving data set for the evaluation of CW/CA algorithms. We first collect manual driving data from the ICCFOT project, then process the data by Kalman smoothing, and finally identify ‘threatening’ and ‘safe’ data sets according to vehicle brake inputs and vehicle range behavior. Five CW/CA algorithms published in the literature are evaluated against the identified data sets. The performance of these algorithms is determined through a performance metric commonly used in signal detection and information retrieval under unbalanced data population.     

Estimation of lateral offset and drift angle for application in secondary collision avoidance system

Recent reports show that the secondary collision on the road gives much higher fatality rate than the other traffic accidents. Many studies have been carried out to prevent the secondary accidents and as a result automotive companies began to introduce brake-based secondary collision avoidance systems. To prevent the secondary accidents it is important to monitor and control the lateral deviation of the vehicle after the primary collision. An estimator for the vehicle’s lateral offset and drift angle based on the in-vehicle sensors and the camera was developed in this paper. By employing sensor fusion scheme and applying extended Kalman filter, the estimator has been designed so that it works even when the camera loses the image of the lanes due to sudden change of the vehicle’s heading angle. For validation of the estimator, simulation has been carried out on various collision scenarios. The simulation results indicated that the estimator of this paper could calculate the vehicle’s lateral deviation with robustness that may be required for application in the secondary collision avoidance systems.     

装载机避障轨迹规划及模型预测轨迹跟踪

轮式装载机在工作区域行驶时，避障过程频繁，以往的避障轨迹规划未考虑整车转向半径约束和车速变化，也较少考虑整车在动力学模型条件下的轨迹跟踪性能。针对上述情况，以自动驾驶轮式装载机为对象，基于最优快速随机扩展树算法（RRT^*），考虑车身膨胀圆个数，生成全局最优避障路径，以整车最小稳定转向半径为约束，利用CC-Steer算法对避障路径进行平滑处理，采用路径-速度分解算法规划满足整车在加速、匀速和减速状态下的避障行驶轨迹。基于整车动力学模型，考虑行驶过程中的横向位置偏差和航向角偏差，并将整车动力传动系统视为1阶惯性环节，构建装载机动力学状态空间方程。以加速度和铰接角为控制输入，以车速、横向位置偏差和航向角偏差为控制输出，建立整车动力学预测模型，以加速度、铰接角和车速为约束条件，将目标函数转换为二次规划问题，建立满足装载机在工作区域避障的模型预测轨迹跟踪控制系统。以规划的非匀速行驶避障轨迹为目标，利用构建的模型预测轨迹跟踪系统，进行自动驾驶轮式装载机的轨迹跟踪仿真。研究结果表明：所提方法能够很好地控制自动驾驶轮式装载机从初始位姿驶向目标位姿，实现整车在工作区域的避障过程，且在避障过程中满足整车的约束要求，保证整车在轨迹跟踪过程中的安全稳定性能。     

Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

ABSTRACT

Collision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require large actuator inputs, together with highly nonlinear tyre cornering response. In order to avoid collision while stabilising autonomous vehicle under dynamic driving situations at handling limits, this paper proposes a novel emergency steering control strategy based on hierarchical control architecture consisting of decision-making layer and motion control layer. In decision-making layer, a dynamic threat assessment model continuously evaluates the risk associated with collision and destabilisation, and a path planner based on kinematics and dynamics of vehicle system determines a collision-free path when it suddenly encounters emergency scenarios. In motion control layer, a lateral motion controller considering nonlinearity of tyre cornering response and unknown external disturbance is designed using tyre lateral force estimation-based backstepping sliding-mode control to track a collision-free path, and to ensure the robustness and stability of the closed-loop system. Both simulation and experiment results show that the proposed control scheme can effectively perform an emergency collision avoidance manoeuvre while maintaining the stability of autonomous vehicle in different running conditions.     

Design and hardware-in-loop implementation of collision avoidance algorithms for heavy commercial road vehicles

An important aspect from the perspective of operational safety of heavy road vehicles is the detection and avoidance of collisions, particularly at high speeds. The development of a collision avoidance system is the overall focus of the research presented in this paper. The collision avoidance algorithm was developed using a sliding mode controller (SMC) and compared to one developed using linear full state feedback in terms of performance and controller effort. Important dynamic characteristics such as load transfer during braking, tyre-road interaction, dynamic brake force distribution and pneumatic brake system response were considered. The effect of aerodynamic drag on the controller performance was also studied. The developed control algorithms have been implemented on a Hardware-in-Loop experimental set-up equipped with the vehicle dynamic simulation software, IPG/TruckMaker®. The evaluation has been performed for realistic traffic scenarios with different loading and road conditions. The Hardware-in-Loop experimental results showed that the SMC and full state feedback controller were able to prevent the collision. However, when the discrepancies in the form of parametric variations were included, the SMC provided better results in terms of reduced stopping distance and lower controller effort compared to the full state feedback controller.