基于异态检测的有轨电车辅助安全防护技术
|
摘要
针对现代有轨电车受运营环境影响,在运行过程中具有一定安全隐患,提出毫米波雷达作为有轨电车异态检测优选手段,并设计辅助安全防护功能,在此基础上,建立新型辅助安全防护算法.提出一种基于异态检测的有轨电车辅助安全防护技术,通过前向检测器实时检测前方轨道障碍物信息,并通过坐标匹配获取前方障碍物与轨道限界的相对位置情况,对于侵入轨道限界的障碍物确定其与列车的轨内距离,实时计算列车防护曲线,可以提前200m距离向驾驶员提供避撞预警信息.仿真试验结果验证了该方法的有效性,能够基于检测器数据实时提供列车防护信息.
For the fact of the modern tram's potential safety risks in different environments,we proposed a millimeterwave radar as the optimized method for modern tram obstacles detection and designed auxiliary safety protection function.Then,a new auxiliary safety protection algorithm was established. A modern tram auxiliary safety protection technology based on obstacles detection was also proposed with the information of anterior obstacles by the detector,and the location of obstacles and relative location of tram rail by coordinate matching.For the obstacles in the range of tram rail,this research could calculate the distance between tram and obstacles and compute train protection curve dynamically,therefore,the warning information could be delievered to the driver.Finally,the results of simulation experiments proved the research,which could provide the dynamic protection information based on the detective data.
For the fact of the modern tram's potential safety risks in different environments,we proposed a millimeterwave radar as the optimized method for modern tram obstacles detection and designed auxiliary safety protection function.Then,a new auxiliary safety protection algorithm was established. A modern tram auxiliary safety protection technology based on obstacles detection was also proposed with the information of anterior obstacles by the detector,and the location of obstacles and relative location of tram rail by coordinate matching.For the obstacles in the range of tram rail,this research could calculate the distance between tram and obstacles and compute train protection curve dynamically,therefore,the warning information could be delievered to the driver.Finally,the results of simulation experiments proved the research,which could provide the dynamic protection information based on the detective data.
引文
[1]陈若望.列车前方障碍物图像检测算法研究[D].成都:西南交通大学,2012.CHEN Ruowang.The algorithm of image processing based on obstacle detection in front of train[D].Chengdu:Southwest Jiaotong University,2012
[2]郑锐.毫米波汽车防撞雷达的设计与实现[D].南京:江苏大学,2010.ZHEN Rui.Design and realization of automotive anti-collision radar[D].Nanjing:Jiangsu University,2010.
[3]徐友春,李克强,连小珉.智能车辆机器视觉发展近况[J].汽车工程,2003,25(5):438.XU Youchun,LI Keqiang,LIAN Xiaomin. The recent development of machine vision for intelligent vehicle[J].Automotive Engineering,2003,25(5):438.
[4] STRMTG.GT3-DTW-obstades fixers V2:ref.4[S].Paris:[s.n.],2010.
[5]沈拓.基于车-车通信的城轨列车追踪预警研究与实现[D].上海:同济大学,2017.SHEN Tuo.Research and realization of city rail train tracking and warming based on inter-vehicle communication[D].Shanghai:Tongji University,2017.
[6]袁志鹏.基于复合序列的城轨列车射频测距与防撞研究[D].上海:同济大学,2014.YUAN Zhipeng.Method of using radio frequency to ranging and anti-collision in city rail train based on composite sequences[D].Shanghai:Tongji University,2014.
[7]WANG Gang,LI Shu,FANG Yungen,et al.Research on improving the accuracy of speed and distance measurement of train by redundant convolution fault-tolerant analysis and multiple data fusion[J].Journal of Applied Sciences,2013,13(12):2300.
[8]WANG Gang, ZENG Xiaoqing,LI Jian. Method for evaluation of railway dynamic safety based on 5 M model and neural network algorithm[J]. Applied Mechanics and Materials,2015,743:445.
[9]WANG Gang,LIU Huaxiang,ZENG Xiaoqing.Study on train headway in different turn-back mode of urban mass transit station[C]//14th World Conference on Transport Research,WCTR2016.Shanghai:Tongji University,2016:600-610.
[10]方云根,曾小清,王刚.轨道交通列控系统共因失效分析[J].上海交通大学学报,2015,49(7):1052.FANG Yungen,ZENG Xiaoqing,WANG Gang.Common cause failure analysis of automatic train control system[J].Journal of Shanghai Jiaotong University,2015,49(7):1052.
[11] ZHENG Wenyi,NAI Wei,DONG Decun,et al.A prognosis method evaluating fault occurance percentage for signaling system in urban rail transit based on naive bayesian classifying[J].Engineering Letters,2013,25(6):100.
[12]NAI Wei,DONG Wei, MAO Jieyi,et al.Performance analysis of broadband multimedia access for vehicles based on optimized IEEE 802.11p[C]//The 79th IEEE Vehicular Technology Conference(VTC-Spring).Qingdao:[s.n.],2014:1901-1905.
[13] ERA.Introduction to ETCS braking curves:ERTMS_040026[S].Paris:[s.n.],1996.
[2]郑锐.毫米波汽车防撞雷达的设计与实现[D].南京:江苏大学,2010.ZHEN Rui.Design and realization of automotive anti-collision radar[D].Nanjing:Jiangsu University,2010.
[3]徐友春,李克强,连小珉.智能车辆机器视觉发展近况[J].汽车工程,2003,25(5):438.XU Youchun,LI Keqiang,LIAN Xiaomin. The recent development of machine vision for intelligent vehicle[J].Automotive Engineering,2003,25(5):438.
[4] STRMTG.GT3-DTW-obstades fixers V2:ref.4[S].Paris:[s.n.],2010.
[5]沈拓.基于车-车通信的城轨列车追踪预警研究与实现[D].上海:同济大学,2017.SHEN Tuo.Research and realization of city rail train tracking and warming based on inter-vehicle communication[D].Shanghai:Tongji University,2017.
[6]袁志鹏.基于复合序列的城轨列车射频测距与防撞研究[D].上海:同济大学,2014.YUAN Zhipeng.Method of using radio frequency to ranging and anti-collision in city rail train based on composite sequences[D].Shanghai:Tongji University,2014.
[7]WANG Gang,LI Shu,FANG Yungen,et al.Research on improving the accuracy of speed and distance measurement of train by redundant convolution fault-tolerant analysis and multiple data fusion[J].Journal of Applied Sciences,2013,13(12):2300.
[8]WANG Gang, ZENG Xiaoqing,LI Jian. Method for evaluation of railway dynamic safety based on 5 M model and neural network algorithm[J]. Applied Mechanics and Materials,2015,743:445.
[9]WANG Gang,LIU Huaxiang,ZENG Xiaoqing.Study on train headway in different turn-back mode of urban mass transit station[C]//14th World Conference on Transport Research,WCTR2016.Shanghai:Tongji University,2016:600-610.
[10]方云根,曾小清,王刚.轨道交通列控系统共因失效分析[J].上海交通大学学报,2015,49(7):1052.FANG Yungen,ZENG Xiaoqing,WANG Gang.Common cause failure analysis of automatic train control system[J].Journal of Shanghai Jiaotong University,2015,49(7):1052.
[11] ZHENG Wenyi,NAI Wei,DONG Decun,et al.A prognosis method evaluating fault occurance percentage for signaling system in urban rail transit based on naive bayesian classifying[J].Engineering Letters,2013,25(6):100.
[12]NAI Wei,DONG Wei, MAO Jieyi,et al.Performance analysis of broadband multimedia access for vehicles based on optimized IEEE 802.11p[C]//The 79th IEEE Vehicular Technology Conference(VTC-Spring).Qingdao:[s.n.],2014:1901-1905.
[13] ERA.Introduction to ETCS braking curves:ERTMS_040026[S].Paris:[s.n.],1996.