The invention discloses a traffic conflict discrimination method based on vehicle motion state time sequence data. The method comprises the following steps: 1, extracting transverse and longitudinal speed and transverse and longitudinal acceleration time domain data of a vehicle in a conflict candidate event in a road intersection range, and making a difference between the transverse and longitudinal speed and transverse and longitudinal acceleration time domain data; 2, converting the time domain data into frequency domain data by using discrete Fourier transform, and calculating the area of a spectrogram; 3, performing data normalization on a data matrix constructed by the area data by adopting zero-mean value normalization, and then performing dimensionality reduction on the matrix data by using a 2DPCA algorithm; and 4, finally, clustering by adopting a fuzzy C-means algorithm, and dividing results into two types of traffic conflicts and non-traffic conflicts in combination with indexes TTC/T2/PET. According to the method, the problems of relatively high subjectivity, relatively low efficiency and the like in traditional traffic conflict judgment are solved, so that the accuracy and transportability of traffic conflict judgment between the motor vehicles can be improved.
本发明公开了一种基于汽车运动状态时序数据的交通冲突判别方法,包括:1提取道路交叉口范围内冲突候选事件中车辆的横纵向速度和横纵向加速度时域数据并作差;2利用离散傅里叶变换将时域数据变换为频域数据并求取频谱图的面积;3采用零‑均值规范化对面积数据所构建的数据矩阵进行数据归一化,继而使用2DPCA算法对矩阵数据进行降维;4最终采用模糊C均值算法进行聚类,并结合指标TTC/T2/PET将结果分为交通冲突与非交通冲突两类。本发明有助于改善传统交通冲突判别中存在的主观性较强、效率较低等问题,从而能提升机动车间的交通冲突判别的精度和可移植性。
Traffic conflict discrimination method based on automobile motion state time series data
一种基于汽车运动状态时序数据的交通冲突判别方法
2024-03-01
Patent
Electronic Resource
Chinese
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