Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 34-39.doi: 10.11896/JsJkx.191100191

• Artificial Intelligence • Previous Articles     Next Articles

Traffic Strategy in Dense Crowd Environments Based on Expandable Path

GAO Qing-Ji, WANG Wen-bo, HOU Shi-hao and XING Zhi-wei   

  1. Institute of Robotics,Civil Aviation University of China,TianJin 300300,China
  • Published:2020-07-07
  • About author:GAO Qing-Ji, born in 1966, ph.D, professor.His main research interests include artificial intelligence and intelligent robot.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (U1533203) and Fundamental Research Funds for the Central Universities of Ministry of Education of China (3122019043).

Abstract: Safe and efficient traffic in dense crowd environments is an important issue to be solved for robots in applications such as airport terminals,etc.The difficulty lies in adapting to the uncertainty of pedestrian behavior and the variability of feasible paths.Referring the social force model of passage and avoidance in crowds,this paper proposed the path expandable view and the traffic strategy in dense crowd environments.Firstly,this paper built the expandable path model,analyzed the space-time relationship between pedestrians and robots,and extracted the expandable path with the path passage probability and credibility.Secondly,the distance convex hull method to select the expandable path set was proposed.On this basis,Breadth First Search was used to establish expandable path set and remove redundant paths.Finally,this paper formulated the traffic strategy of robots in various environments according to the optimal path evaluation function and the right of way rule.Simulation results show that the method can achieve higher traffic efficiency in dense crowd environments.

Key words: Dense crowd, Expandable path, Passage probability, Credibility, Traffic strategy

CLC Number: 

  • TP242
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