Abstract
While the risk from the obstacle could significantly alter the navigation path of a pedestrian, this problem is often disregarded by many studies in pedestrian simulation, or is hindered by a simplistic simulation approach. To address this problem, we proposed a novel simulation model for the local path-planning process of the pedestrian agent, adopting reinforcement learning to replicate the navigation path. We also addressed the problem of assessing the obstacle’s risk by determining its probability of collision with the obstacle, combining with the danger from the obstacle. This process is subsequently incorporated with our prediction model to provide an accurate navigation path similar to the human thinking process. Our proposed model’s implementation demonstrates a more favorable result than other simulation models, especially in the case of the obstacle’s appearance. The pedestrian agent is capable of assessing the risk from the obstacle in different situations and adapting the navigation path correspondingly.
Highlights
Accurate replication of the human navigation behavior in a pedestrian simulation model plays an important role in the studies within the safety domain
For the training task of the pedestrian agent, we adopted the reinforcement learning library ML-Agents [44], which acts as a communicator between Unity and Python machine learning code
We have developed a novel pedestrian path-planning model using reinforcement learning while considering the prediction of the obstacle’s movement and the risk from the obstacle
Summary
Accurate replication of the human navigation behavior in a pedestrian simulation model plays an important role in the studies within the safety domain. Along with the rising trend of autonomous vehicles, pedestrian simulation studies have attracted increasing interest, especially in the situation of crossing with vehicles, to avoid possible fatal accidents [4,5] While these studies could construct a sufficient reproduction of the pedestrian navigation behavior in certain applications, for example, the robot movement in pedestrian roads, their approaches might not be able to provide a human-like behavior needed for some research, in risk and safety problems for instance. An action could receive a positive reward, but that action may eventually lead to a worse result Because of this reason, the goal of a reinforcement learning agent is to optimize the policy to achieve an advantageous cumulative reward
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