ITS系统防碰撞技术研究
发布时间：2018-08-19 13:48
【摘要】：近年来,随着城市化进程的加快,交通网络越来越复杂。随之而来的追尾碰撞事故发生频率越来越高,然而如何有效地检测追尾碰撞事故的发生成为当前智能交通领域急需解决的问题。本文试图用信号检测的方法来攻克此问题。本文提出了一套追尾碰撞检测方案来检测追尾事件的发生。追尾碰撞检测方案分为两部分:追尾碰撞模型和信号检测模型。追尾碰撞模型用来检测当前车辆与后方车辆是否存在追尾的可能。如果发现危险情况,就按照既定的信号模型一直产生报警信号直到危险情况解除。而后方车辆的信号检测模型一直在检测前方是否有车辆发出报警信号,如果检测到报警信号,就立刻通知司机进行制动来降速或者停车。从而将目标问题转化为如何高性能地检测报警信号。针对于该问题,本文提出两种信号检测模型:空间相关信号检测模型和时间序列检测模型。两种检测模型分别利用采集样本的空间相关性和时间相关性,提高其检测概率,增加等检测概率条件下的检测距离,给后车司机更多的反应时间。论文还提出检测性能指标来评价信号检测模型的性能优劣。空间相关信号检测模型利用空间相关性构建信号检测器,来处理信号样本。本文考察了能量检测器(Energy detector,ED),能量联合检测器(AND规则、OR规则)以及协方差检测器(Covariance detector,CD),推导了这些检测器的检验统计量、检测门限值和检测概率公式。理论分析比较了能量联合检测器(AND和OR规则)和协方差检测器的性能。通过模拟实验验证了理论分析的结论:协方差检测器的检测性能比能量联合检测器(AND、OR规则)高出50%以上。时间序列检测模型利用时间相关性构建时间序列检测器,经过时间序列检测器来处理采样样本得到检验统计量。本文设计了三种时间序列检测器:逻辑联合检测器、比值联合检测器以及仲裁联合检测器。推导了每种检测器的检验统计量、检测门限值公式。通过模拟实验分别对三种检测器进行检测性能进行比较和分析,得出结论:在判决周期中存在三个时间序列时,逻辑联合检测器的检测性能最好;比值联合检测器次之;仲裁联合检测器最坏。结合信号检测模型的检测性能指标,将两种检测模型的信号检测器进行性能比较。并得出结论:1)利用空间相关性和时间相关性可提高信号检测模型的检测概率,增加信号检测模型的检测距离;2)逻辑联合检测器的检测性能最好。
[Abstract]:In recent years, with the acceleration of urbanization, the traffic network is becoming more and more complex. The frequency of rear-end collision is becoming higher and higher. However, how to effectively detect the rear-end collision has become an urgent problem in the field of intelligent transportation. This paper attempts to solve this problem by signal detection. In this paper, a rear-end collision detection scheme is proposed to detect the occurrence of rear-end events. The rear-end collision detection scheme is divided into two parts: the rear-end collision model and the signal detection model. The rear-end collision model is used to detect whether there is a possibility of rear-end between the current vehicle and the rear vehicle. If a dangerous situation is found, follow the established signal model to generate an alarm signal until the danger is removed. The signal detection model of the rear vehicle always detects whether there is an alarm signal in front of the vehicle. If the alarm signal is detected, the driver is immediately informed to brake to slow down or stop. Thus, the target problem is transformed into how to detect the alarm signal with high performance. To solve this problem, two signal detection models are proposed in this paper: spatial correlation signal detection model and time series detection model. The two detection models improve the detection probability and the detection distance under the condition of equal detection probability by using the spatial correlation and time correlation of the collected samples respectively and give the driver more reaction time. The performance index is also proposed to evaluate the performance of the signal detection model. Spatial correlation signal detection model uses spatial correlation to construct signal detector to process signal samples. In this paper, the Energy detector (Ed), the energy joint detector (AND rule OR rule) and the Covariance detector (Covariance detector CD) are investigated. The test statistics, detection threshold values and detection probability formulas of these detectors are derived. The performance of energy joint detector (AND and OR rule) and covariance detector are analyzed and compared theoretically. The conclusion of the theoretical analysis is verified by simulation experiments: the detection performance of the covariance detector is more than 50% higher than that of the energy joint detector (ANDOR rule). Time series detection model uses time correlation to construct time series detector, which is used to process samples to obtain test statistics. In this paper, three kinds of time series detectors are designed: logic joint detector, ratio joint detector and arbitration joint detector. The test statistics and detection threshold formula of each detector are derived. By comparing and analyzing the detection performance of three kinds of detectors by simulation experiments, it is concluded that when there are three time series in the decision period, the detection performance of the logic joint detector is the best, the ratio joint detector is the second. The arbitration joint detector is the worst. Combining the detection performance index of the signal detection model, the performance of the two detection models is compared. It is concluded that the detection probability of signal detection model can be improved by using spatial correlation and temporal correlation, and the detection distance of signal detection model can be increased. 2) the detection performance of logic joint detector is the best.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495
，
本文编号：2191838
[Abstract]:In recent years, with the acceleration of urbanization, the traffic network is becoming more and more complex. The frequency of rear-end collision is becoming higher and higher. However, how to effectively detect the rear-end collision has become an urgent problem in the field of intelligent transportation. This paper attempts to solve this problem by signal detection. In this paper, a rear-end collision detection scheme is proposed to detect the occurrence of rear-end events. The rear-end collision detection scheme is divided into two parts: the rear-end collision model and the signal detection model. The rear-end collision model is used to detect whether there is a possibility of rear-end between the current vehicle and the rear vehicle. If a dangerous situation is found, follow the established signal model to generate an alarm signal until the danger is removed. The signal detection model of the rear vehicle always detects whether there is an alarm signal in front of the vehicle. If the alarm signal is detected, the driver is immediately informed to brake to slow down or stop. Thus, the target problem is transformed into how to detect the alarm signal with high performance. To solve this problem, two signal detection models are proposed in this paper: spatial correlation signal detection model and time series detection model. The two detection models improve the detection probability and the detection distance under the condition of equal detection probability by using the spatial correlation and time correlation of the collected samples respectively and give the driver more reaction time. The performance index is also proposed to evaluate the performance of the signal detection model. Spatial correlation signal detection model uses spatial correlation to construct signal detector to process signal samples. In this paper, the Energy detector (Ed), the energy joint detector (AND rule OR rule) and the Covariance detector (Covariance detector CD) are investigated. The test statistics, detection threshold values and detection probability formulas of these detectors are derived. The performance of energy joint detector (AND and OR rule) and covariance detector are analyzed and compared theoretically. The conclusion of the theoretical analysis is verified by simulation experiments: the detection performance of the covariance detector is more than 50% higher than that of the energy joint detector (ANDOR rule). Time series detection model uses time correlation to construct time series detector, which is used to process samples to obtain test statistics. In this paper, three kinds of time series detectors are designed: logic joint detector, ratio joint detector and arbitration joint detector. The test statistics and detection threshold formula of each detector are derived. By comparing and analyzing the detection performance of three kinds of detectors by simulation experiments, it is concluded that when there are three time series in the decision period, the detection performance of the logic joint detector is the best, the ratio joint detector is the second. The arbitration joint detector is the worst. Combining the detection performance index of the signal detection model, the performance of the two detection models is compared. It is concluded that the detection probability of signal detection model can be improved by using spatial correlation and temporal correlation, and the detection distance of signal detection model can be increased. 2) the detection performance of logic joint detector is the best.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495
，
本文编号：2191838
本文链接：https://www.wllwen.com/kejilunwen/daoluqiaoliang/2191838.html
