Articles | Volume 15, issue 1
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

Related subject area

Subject: Dynamics and Control | Techniques and Approaches: Mathematical Modeling and Analysis
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Cited articles

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Chen, H. and Zhang, X.: Path planning for intelligent vehicle collision avoidance of dynamic pedestrian using Att-LSTM, MSFM, and MPC at unsignalized crosswalk, IEEE T. Ind. Electron., 69, 4285–4295,, 2021. 
Eiffert, S., Li, K., Shan, M., Worrall, S., Sukkarieh, S., and Nebot, E.: Probabilistic Crowd GAN: Multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian attention network, IEEE Robot. Autom. Let., 5, 5026–5033,, 2020. 
Fildes, B., Keall, M., Bos, N., Lie, A., Page, Y., Pastor, C., Pennisi, L., Rizzih, M., Thomas, P., and Tingvall, C.: Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes, Accident Anal. Prev., 81, 24–29,, 2015. 
Gao, H., Su, H., Cai, Y., Wu, R., Hao, Z., Xu, Y., Wu, W., Wang, J., Li, Z., and Kan, Z.: Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections, Sci. China Inform. Sci., 64, 1–21,, 2021. 
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.