Vision-Based Vehicle Detection for a Forward Collision Warning System

A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition;2) the local features are mutually processed to improve the reliability of vehicle detection;3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions;4) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.

Application of Machine Vision to Vehicle Automatic Collision Warning Algorithm

Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system（CWS） can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver＇s driving behavior, algorithm＇s warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm（CWA） model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.

The Study of Wireless Collision Avoidance and Early Warning System in Metro Vehicles

The basic composition and working principle of wireless collision avoidance and early warning system based on spread spectrum ranging which is used in urban mass transit is introduced in this paper. Some performance indicators such as maximum measured distance and range errors are theoretically analyzed and numerically calculated. According to the characteristics of the urban mass transit, the applicability of the system is evaluated.

Developing Framework to Test Driving Performance at Left-Turn Movement with In-Vehicle Advance Collision Warning Message

Left-turning traffic without a protected left-turn signal is one of the major safety concerns at urban intersections. Though an average of only l0% - 15% of all approaching traffic turns left, significantly a large proportion of left-turn crashes occur involving 21% of all intersection fatal crashes. Where traditional safety countermeasures of signal timing-phasing and use of flashing yellow light have reportedly failed to significantly reduce the rate of crashes, an in-vehicle advance collision warning message can be helpful to reduce left-turn collisions at intersections. In this study, an in-vehicle audio warning application has been designed by providing two safety warning messages (Advance Warning Message and Safe Left-turn Maneuver Message) under the vehicle to vehicle (V2V) communication system, which is triggered based on the acceptable gaps of oncoming opposing vehicles for a safe left-turn. A driving simulator test has been conducted with 30 participants to investigate the impacts of warning messages on performance measures such as speed and acceleration profiles, collision records, brake reaction distance, and intersection clearance time. Statistical results showed that with the help of these messages, all participants were able to reduce speeds and accelerations and chose suitable gaps without potential conflicts. Moreover, the results of questionnaire analysis provide a positive acceptability especially for the Safe Left-turn Maneuver Message. Based on the performance measurements, this type of safety warning messages can be recommended for possible real-road tests for practical applications.

数字孪生驱动的掘锚设备跟踪定位与碰撞检测方法研究

掘锚自动化作业是煤矿巷道智能掘进的关键,针对当前掘锚设备交替作业过程中相对位姿测量和碰撞检测难题,提出一种数字孪生驱动的煤矿井下掘锚设备跟踪定位与碰撞检测方法。首先,为克服井下掘进工作面低照度、高粉尘、复杂背景干扰的影响,以多点红外LED标靶作为信息源,通过工业相机采集红外LED特征点图像,利用Hough轮廓检测与质心法提取光斑中心并通过二进制编码识别标靶ID,采用改进稀疏光流算法对光斑进行跟踪,同时建立基于PNP的掘锚设备位姿解算模型,采用对偶四元数获得设备间相对位姿。其次,利用数字孪生技术,基于Unity3D平台建立对应实际尺寸的掘锚设备及工作面数字孪生模型,利用Socket通信方式实现虚拟空间与物理实体之间的实时数据传输与交互,在虚拟空间中实现掘锚设备实时位姿的三维可视化,结合任意多边形OBB(orientedboundingbox)碰撞检测算法,实现掘锚设备虚拟碰撞检测。最后,搭建实验平台进行掘锚设备位姿测量试验,同时对虚实运动轨迹和碰撞检测效果进行验证。实验结果表明:掘锚设备跟踪定位实验的位置误差不超过20mm,角度误差不超过0.30°;虚实位置坐标对比中X轴方向最大误差不超过1.14mm;Y轴方向最大误差不超过1.10mm,能够保证系统虚实一致性和同步性,满足掘进工作面作业过程中掘锚设备实时跟踪定位及碰撞检测的要求。

ADAS碰撞和车道偏离检测系统设计与实现

为满足交通运输领域对高级驾驶辅助系统(Advanced Driving Assistance System,ADAS)前车碰撞和车道偏离预警性能的检测和质量评价,解决手动测试中不准确、效率低的问题,设计一种能应用于实车测试的高精度动态检测系统。综合运用高精度定位技术和组合导航技术提高系统的测量精度和可靠性,使用自动化检测软件实现人机交互、测量数据分析及运动轨迹直观显示。实验结果表明,系统工作状态稳定,测量精度高,可准确评价ADAS系统碰撞和车道偏离预警性能。

无信号交叉口网联车辆协同碰撞预警研究进展

为明晰无信号交叉口网联车辆协同碰撞预警研究的局限性及发展方向,系统梳理了协作式交叉口碰撞预警(CICW)的研究进展。首先,分类梳理了现有交叉口行车冲突检测方法的优势与不足;其次,总结了不同预警级别、机制和模式的适用性;再次,归纳了CICW有效性及安全性评价指标,对比了仿真、实车以及虚实融合测试的技术特征;然后,分析了驾驶人不确定性与通信不可靠对CICW的影响及优化思路;最后,对CICW的发展方向进行了展望。结果表明:行车风险场模型是解决现有CICW冲突检测方法中综合交通风险、冲突严重程度表征问题的可行方案,但仍需进一步研究适当的环境参数、风险指标及阈值的设定;CICW应用场景下的驾驶人驾驶特征的建模、预测和在线识别,以及CICW预警模式的自适应构建是设计可靠有效CICW应用的可行解决思路;实现对CICW的全方面客观评估,有赖于研究搭建综合评价机制及大规模试验平台;车联网通信不可靠严重影响CICW的有效性,需要进一步研究信道拥塞控制机制与基于通信故障/失效预测的CICW容错机制。

基于行车风险场的交通冲突风险预警方法

城市交叉路口是交通事故多发路段,对于智能网联汽车来说,在行驶过程中进行风险检测与碰撞预警,从而保障驾驶的安全性至关重要。本文针对有信号灯的城市交叉路口提出一种考虑信号灯约束的行车风险场模型,并基于此模型设计三级碰撞预警方法。首先针对城市交叉路口的潜在冲突风险点构建功能场景,并将信号灯的约束作用考虑进行车风险场模型。为了解决碰撞预警问题,提出以TTC为指标划分三级冲突区域,通过计算主车行驶过程中周围对应的场强值,并根据干扰车在主车周围势能场的位置来衡量主车所受碰撞风险。实验结果表明,所设计的模型能够准确对进入主车势能场范围的干扰车进行预警,预警成功率可达100%,误报率仅为3.4%,证明所提方法的可靠性和有效性。

基于安全距离和时空网格的交叉路口车辆防碰撞安全预警

为保证交叉路口车辆行驶不出现碰撞问题,提出一种基于安全距离和时空网格的交叉路口车辆防碰撞安全预警方法。将交叉路口划分为多个网格,利用路侧单元并根据车辆所占的网格数,初步判断是否存在两车共用同一网格的情况。若在某一时刻两车占用相同的网格则会发生碰撞,故依据交叉路口车辆防碰撞安全距离,计算交叉路口前车未启动、前车匀速驾驶、前车均匀减速驾驶三种工况的临界行驶安全距离,结合此距离,将预警级别分为黄、绿、红三级,进行分级安全预警。实验结果显示:采用所提方法后,交叉路口车辆均安全通行,能够降低交叉路口车辆防碰撞安全预警的误警率、预警虚报率,提高交叉路口车辆防碰撞安全预警精度。

考虑通信延迟的桥梁防撞自动预警方法

在开展桥梁超高防撞自动预警时,通信延迟现象会直接影响预警方法的预警效果,从而威胁船只的安全运行,为此提出考虑通信延迟的桥梁防撞自动预警方法。具体分析防撞预警的影响因素,基于分析结果建立通信延迟模型计算出数据预警通信时的探测时延,根据计算结果,在考虑通信延迟的情况下完成采集数据;再根据采集的数据确定防撞预警准则,获取隶属度函数以及相关约束条件;结合模糊神经网络建立桥梁的防撞预警模型,依据该模型完成桥梁的防撞自动预警。实验结果表明,使用该方法开展防撞预警,预警效果较好。

基于改进YOLOv5及危险区域判断的碰撞预警系统研究

为提升碰撞预警系统对周围环境的感知能力,提出一种基于YOLOv5及危险区域判断的碰撞预警系统。首先,通过通道注意力模块提高模型的判别能力和准确性,然后,使用路径聚合网络与空间金字塔池化提高模型对多尺度特征的提取能力,最后,通过引入预警激活区域过滤相对安全的目标,提高了预警系统的预警精确度。结果表明,引入预警激活区域后,与无预警激活区域相比,预警系统的准确度、精度和召回率分别提高20%、50%和26.7%,运行速度提升49.1%,进一步证明了方法的有效性。

基于3D蒙特卡洛的TCAS告警下的碰撞风险评估

对于同时拥有运输和飞行训练任务的单跑道机场,运行过程中常出现为了避免与运输航班发生冲突而延长训练机三边的情况,从而导致运输机的交通碰撞避免系统(TCAS)产生TA告警。提出了一个3D碰撞风险评估模型来评估此场景下两机的碰撞风险。建立了飞机位置预测模型,使用蒙特卡洛方法得出飞机在每一时刻的位置分布;根据TCAS系统定义建立了TCAS碰撞模型;最后,统计训练机的蒙特卡洛样本进入TCAS碰撞模型的次数,对飞机间的碰撞风险做出评估。结果表明:三边航线与跑道间的侧向间距不小于2037 m时,两机触发TA告警后,两机可以分别沿三边以及五边继续飞行。在后续的飞行过程中,两机的碰撞风险小于ICAO规定的1×10-7次/h,满足安全目标水平。

航道桥梁主被动防撞智慧预警系统研究

船舶与桥梁碰撞的风险持续升高,航道桥主被动防撞装置系统逐渐被重视。为了保障航道桥和船只的安全运行,本文研究了航道桥主被动防撞智慧预警系统。通过在航道桥上安装视频监控设备、测高设备、主控设备、偏航监测设备、声光报警控制设备,再配备相关技术进行设备的智慧化协同运行,实现航道桥主被动防撞智慧预警,达到保护桥墩的作用。同时监控系统会全程记录此过程,并上报管理中心,为以后船撞事故责任划分提供依据。

基于深度学习的汽车防追尾预警系统设计

当前主流的防追尾系统普遍存在识别因素单一、对驾驶员自身预防效果欠佳的问题,为此,设计一种基于深度学习的汽车防追尾预警系统。将YOLOv5剪枝技术、注意力机制、PID优化器等方法融入网络模型的训练中,以优化模型精度并减小模型体积;其次,以距离判断为主,速度、加速度、事故危害性判断为辅来计算车辆的追尾风险,每次预警后通过MQTT协议将数据上传至物联网平台,并在系统结束运行时对驾驶员进行安全分析。系统最终部署在TensorRT环境上进行再次优化。实验结果表明,所设计的汽车防追尾预警系统响应速度快,适应性强,判断风险较为准确。

马房北江大桥防船撞预警系统现场测试方法

介绍了马房北江大桥防船撞预警系统的测试原理和功能,提出了预警系统的现场测试指标和技术要求。通过对系统水位计的示值误差、通航实时净高示值误差、船舶水上高度示值误差和船舶距桥梁距离误差等指标的现场测试,验证了该系统使用的准确性。针对防船撞预警系统,提出需进一步研究的问题和方向。

门座式起重机无人自动化作业安全预警系统设计

开发一种门座式起重机无人自动化作业的安全预警系统。通过对作业方式的研究,利用GPS和激光雷达的融合技术,精确规划出安全作业区域,并计算出所需点位的三维坐标信息。根据这些坐标,设定了合适的安全距离和极限位,以确保设备在自动化作业时遇到突发情况能够安全停机,从而降低事故发生的风险。该安全预警系统能够有效地提高门座式起重机无人自动化作业的安全性和稳定性,为工业生产的安全和效率提供有力保障。

路网级桥梁集群监测系统建设关键技术及实践

桥梁健康监测是防范结构运行风险、提升养护管理和应急处置水平的重要举措之一,国家和行业政策对于桥梁结构健康监测系统建设提出了明确的要求。以广东交通集团路网节点桥梁集群监测系统为例,介绍了路网级桥梁监测系统的技术路线、总体架构、关键技术和建设实践,可为地市级、路网级桥梁健康监测系统的构建提供参考。

Evaluating the moderating effect of in-vehicle warning information on mental workload and collision avoidance performance

Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload.However,whether moderation and mediation effects exist among warning information,driver’s cognition,behavior and risky avoidance performance is unclear.Design/methodology/approach–This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior.A driving simulator experiment was conducted with waring and command information.Findings–Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario.The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk.At the same time,command information can weaken this negative effect.Moreover,improved collision avoidance behavior can be achieved through increasing drivers’mental workload.Practical implications–The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior,thus contributing to improved in-vehicle information system design.Originality/value–The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’collision avoidance ability.Through further calibration with larger sample size,the proposed structural model can be used to predict the effect of invehicle warnings in different risky driving scenarios.

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

Purpose–The purpose of this paper is to develop a proof-of-concept(POC)Forward Collision Warning(FWC)system for the motorcyclist,which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle(motorcycle).Design/methodology/approach–This comes in three approaches.First,time-to-collision value is to be calculated based on low-cost camera video input.Second,the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate.Third,the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.Findings–This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above.First,to predict time-to-collision,nested Kalmanfilter and vehicle detection is used to convert image pixel matrix to relative distance,velocity and time-to-collision data.Next,for trajectory prediction of detected vehicles,a few algorithms were compared,and it was found that long short-term memory performs the best on the data set.The lastfinding is that to determine the leaning direction of the ego vehicle,it is better to use lean angle measurement compared to riding pattern classification.Originality/value–The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle(motorcycle).

移动边缘计算辅助智能驾驶中基于高效联邦学习的碰撞预警算法

智能驾驶中的碰撞避免任务存在对时延要求极高和隐私保护等挑战。首先,该文提出一种基于自适应调整参数的半异步联邦学习(SFLAAP)的门控循环单元联合支持向量机(GRU_SVM)碰撞多级预警算法,SFLAAP可根据训练和资源情况动态调整两个训练参数:本地训练次数和参与聚合的局部模型数量。然后,为解决资源受限的移动边缘计算(MEC)下碰撞预警模型协作训练的效率问题,根据上述参数与SFLAAP训练时延的关系,建立训练总时延最小化模型,并将其转化为马尔可夫决策过程(MDP)。最后,在所建立的MDP中采用异步优势演员-评论家(A3C)学习求解,自适应地确定最优训练参数,从而减少碰撞预警模型的训练完成时间。仿真结果表明,所提算法有效地降低训练总时延并保证预测精度。