applied sciences  
The Impact of Obstacle's Risk in Pedestrian Agent's Local Path-Planning  
[PERSON],*(r) and [PERSON]  
Footnote 1  
As an example, in the pedestrian path-planning process, the two following tasks are carried out sequentially. In the global path-planning task, the pedestrian uses his experience and knowledge to specify his destination and plan the route to get there. In the local path-planning task, the surrounding environment is often observed via human vision and transformed into a topological map. Subsequently, the pedestrian estimates the path would be taken before carrying out the actual movements [6]. While there is a great deal of research that addresses the global path-planning, the route selection process to the destination [7; 8], the studies of the local path-planning problem are generally scarce. For the few studies that focus on this problem, their models often try to optimize certain objectives, such as next state optimizing [9] or way finding [10]. In real life, people tend do not usually choose the most optimized solution [11]; therefore, these models may yield inaccurate navigation behavior in certain situations.  
Addressing the obstacle's risk and danger is also a factor that is often overseen by many studies. Although the majority of research in pedestrian simulation considers the obstacle in collision avoidance, to our best knowledge, not many studies have addressed how its danger affects the pedestrian's choice. For the papers that discuss this problem [12], the models proposed are quite limited in using the empirical approach without considering the human cognitive factors. The results of these models could be consequently insufficient, especially in the case the danger of the obstacle greatly alters the path choice of the pedestrian. For safety-focused applications, this problem could produce undesirable results, possibly causing significant consequences as a result.  
For that reason, we proposed a novel pedestrian simulation model focusing on the local path-planning process, considering the obstacle's danger and risk assessment while taking account of human cognitive factors. Our model adopts the concept of deep reinforcement learning (RL), a neural network-based machine learning technique, for the training of the pedestrian agent. The approach is inspired by the mechanism of human cognitive system. More specifically, in reinforcement learning, the agent learns to take actions depending on the states of the environment based on the proper rewarding, similar to human's trial-and-error learning approach. Deep reinforcement learning approaches also employ artificial neural networks, which were inspired by the mechanisms of the biological neural network in the human brain. Thanks to that, the aspects in obstacle's danger and risk assessment are further explored in a similar mean as to how humans address dangers in real life. The implementation of our model has demonstrated favorable results. The pedestrian agent in our model was able to plan a more realistic navigation path compared to traditional models, especially in the case of interacting with an obstacle within the environment.  
The remainder of this paper is structured as follows. Section 2 presents the studies related to our research, followed by Section 3 which briefly explains the background of the concepts mentioned in this paper. Section 4 introduces the main methodology of our research, consisting of the path-planning training, point-of-conflict predicting, and obstacle danger and risk assessment, which later represented comprehensively in Section 5, Sections 6 and 7, respectively. Subsequently, Section 8 demonstrates the results of our implementation. Section 9 gives our discussion and finally, and Section 10 concludes our paper.