The patent discloses a running condition monitoring method of an intelligent vehicle-road system. The method comprises the steps that an index matrix is established based on dimensions such as intersections, road sections and road nets, and a running condition index set is obtained; aiming at a characteristic that a lot of index parameters exist and dimensions are different, values are normalizedthrough a threshold method; an entropy weight method is used for judging a dispersion degree of each index and calculating a weight occupied by each index; a calculation method of weighting total root-mean-square deviations is used for measuring calculation deviations between the index values and reflect accuracy of measurement; calculation of entropy weight-weighting total root-mean-square deviations is used for combined empowerment of the index set; and the index set is import into a deep reinforcement learning neural network, a gate control structure is used for multi-layer convolution, characteristic values are extracted, the reinforcement leaning network is applied, an optimal index set is obtained through training of a maximized target function, and comprehensive analysis of IVIS running conditions is realized. By the scheme, possibilities in an index absolute set are avoided rationally, and accuracy of the index set is greatly increased.
本专利公开了一种智能车路系统的运行状况监测方法,所述方法包括:从交叉口、路段、路网等维度建立指标矩阵,得出运行状况指标集。针对指标参数多且量纲不同的特性,采用阈值法进行数值的规范化处理。运用熵权法判断各指标的离散程度,计算指标所占的权重。运用加权总均方根偏差计算法,来衡量指标值之间的计算偏差,反映出测评的精确度。利用熵权‑加权总均方根偏差计算对指标集进行组合赋权。将指标集导入深度强化学习神经网络,利用门控结构进行多层卷积,提取特征值,运用强化学习网络,通过训练最大化目标函数得到最优指标集,进行IVIS运行状况的综合分析。通过上述方案,合理规避了指标绝对集中的可能性,大大提高了指标集的精确性。
Running condition monitoring method of intelligent vehicle-road system
一种智能车路系统的运行状况监测方法
2020-07-24
Patent
Elektronische Ressource
Chinesisch
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