## 2 Related Works  
Many studies propose pedestrian simulation models by using physics-based concepts, such as Newtonian mechanics or fluid dynamics [13]. One of the most influential models in pedestrian simulation Social Force Model (SFM), employs the concept of forces to the pedestrians and the obstacles [14]. The idea of this model is to treat every object within the environment as a force-based object, which impulses and attracts each other similar to magnets. Based on the concept of SFM, several studies have proposed other factors which could affect the pedestrian agent's navigation, such as heading direction [15] or the connection between speed and density [16]. Apart from force-based models, some modelsapproach the problem by utilizing fluid dynamics to simulate the motion of the pedestrian crowds [17]. Considering the pedestrian path-planning problem, while the behaviors replicated from these models could be sufficient in some basic circumstances; in most cases, there is a large distinction between the behavior and the anticipation within human thoughts. A possible cause is that humans rarely think of animals or other pedestrians as physical objects affected by different forces.  
Besides physics-based models, other studies adopt the agent-based approach to model the pedestrian simulation. Compared to force-based models, adopting human thinking is more accessible in agent-based ones. Several studies, mostly in the robotic domain, have tried to simulate human behaviors in their models by proposing various concepts. For instance, [PERSON] et al. [18] introduced the concept of human comfort, indicating the factors that affect how the movement makes humans feel comfortable. Another example is a study by [PERSON] et al. [19], in which the pedestrian agent could consider its decision between exploitation and exploration. In addition, there are several studies adopting reinforcement learning for the pedestrian agent. For example, [PERSON] et al. [20] employed the q-learning algorithm to implement a multi-agent navigation system. These agent-based models could replicate basic situations, but for more complicated ones, especially when considering the danger of obstacles, such models have certain limitations. In our previous study [21], we have addressed the path-planning problem in accordance to obstacle's danger; however, the model is not well scalable in different environments. In addition to reinforcement learning, inverse reinforcement learning could also be adopted [22], but it is hard to export human behavior information from existing available datasets.  
Regarding the problem of obstacle prediction, there have been many studies addressing different problems in the prediction of obstacles and pedestrians. Many of these studies depend on the processing of image or video data [23; 24; 25]. Others suggest predicting the movement of pedestrian obstacles based on human behaviors [26], or body language [27; 28]. Several studies approach this problem by using a map of probability [29; 30]. We also proposed an approach for obstacle prediction by introducing the concept of point-of-conflict [31], which performs well in both cases of moving obstacles and pedestrians.