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.
Running condition monitoring method of intelligent vehicle-road system