Articles | Volume 15, issue 1
https://doi.org/10.5194/ms-15-17-2024
https://doi.org/10.5194/ms-15-17-2024
Research article
 | 
18 Jan 2024
Research article |  | 18 Jan 2024

Development of pedestrian collision avoidance strategy based on the fusion of Markov and social force models

Bin Tang, Zhengyi Yang, Haobin Jiang, and Zitian Hu

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Short summary
A pedestrian motion fusion model is constructed to predict pedestrian trajectory by the fusion of a Markov model and an improved social force model with a multiple linear regression algorithm. The parameters in the model are calibrated by the maximum likelihood estimation method. Based on pedestrian trajectory prediction, a longitudinal and lateral collision avoidance control strategy is developed. The results show that the proposed strategy can effectively ensure the safety of pedestrians.