Abstract
In order to take into account evading mechanism and make more realistic simulation results, we propose a data-driven and collision-free hybrid crowd simulation model in this paper. The first part of the model is a data-driven process in which we introduce an algorithm called MS-ISODATA (Main Streams Iterative Self-organizing Data Analysis) to learn motion patterns from real scenarios. The second part introduces an agent-based collision-free mechanism in which a steering approach is improved and this part uses the output from the first part to guide its agents. The hybrid simulation model we propose can reproduce simulated crowds with motion features of real scenarios, and it also enables agents in simulation evade from mutual collisions. The simulation results show that the hybrid crowd simulation model mimics the desired crowd dynamics well. According to a collectiveness measurement, the simulation results and real scenarios are very close. Meanwhile, it reduces the number of virtual crowd collisions and makes the movement of the crowd more effective.
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This work is supported by national natural science foundation of China under grants 61771145 and 61371148.
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Cheng, Q., Duan, Z., Gu, X. (2018). Data-Driven and Collision-Free Hybrid Crowd Simulation Model for Real Scenario. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_6
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