A q-learning pedestrian evacuation simulation method and system based on artificial neural network

Abstract

The invention relates to a pedestrian evacuation simulation method and system adopting an artificial neural network-based Q-Learning algorithm. The method comprises the steps that to-be-evacuated pedestrians are divided into a plurality of groups according to initial related parameters, a leader is selected in each group, and the rest of the to-be-evacuated pedestrians are taken as followers; in each group, the leader preferentially learns and selects an optimal path obtained by performing global planning on evacuation paths through the artificial neural network-based Q-Learning algorithm, and the followers calculate a resultant force of the followers, the pedestrians in the group, the pedestrians between the groups, and environments according to a social force model, avoid barriers and follow the leader; and the to-be-evacuated pedestrians are all evacuated. According to the method and the system, global path planning is carried out in combination with the advantages of reinforcement learning and an artificial neural network; the deficiencies of pure reinforcement learning are made up for; a bottom layer matches with the social force model to guide a movement; and quick and effective way finding and relatively real evacuation of crowds can be realized.

Description

technical field [0001] The invention belongs to the technical field of crowd evacuation simulation, and in particular relates to a Q-Learning pedestrian evacuation simulation method and system based on an artificial neural network. Background technique [0002] In recent years, with the increase in the number of large public places, pedestrian crowding can be seen everywhere. In densely populated public places, the lack of pedestrian safety awareness and unfamiliarity with the surrounding environment have hidden great potential safety hazards, especially when disasters such as fires occur, the rapid and safe evacuation of people has become an urgent problem to be solved. For public places with a large flow of people and highly variable personnel composition, pedestrians often do not know enough about the environment, and it is difficult to simulate various scenarios at a low cost by using traditional evacuation drills. Using computer simulation technology to carry out scene...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the above problems, the present invention provides a Q-Learning pedestrian evacuation simulation method and system based on artificial neural network. The present invention combines the advantages of reinforcement learning and artificial neural network for global path planning, making up for simple reinforcement learning. The bottom layer cooperates with the social force model to guide the movement, effectively realizing the rapid and effective wayfinding and more realistic evacuation of the crowd

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