A pedestrian path-planning model in accordance with obstacle's danger with reinforcement learning

TL;DR

This paper introduces a reinforcement learning-based pedestrian path-planning model that incorporates obstacle danger perception, resulting in more human-like navigation behavior compared to traditional force-based models.

Contribution

The study presents a novel reinforcement learning approach that models human perception of obstacle danger, improving the realism of pedestrian navigation simulations.

Findings

Path-planned trajectories resemble human pedestrian behavior

Model effectively incorporates obstacle danger awareness

Outperforms traditional force-based models in realism

Abstract

Most microscopic pedestrian navigation models use the concept of "forces" applied to the pedestrian agents to replicate the navigation environment. While the approach could provide believable results in regular situations, it does not always resemble natural pedestrian navigation behaviour in many typical settings. In our research, we proposed a novel approach using reinforcement learning for simulation of pedestrian agent path planning and collision avoidance problem. The primary focus of this approach is using human perception of the environment and danger awareness of interferences. The implementation of our model has shown that the path planned by the agent shares many similarities with a human pedestrian in several aspects such as following common walking conventions and human behaviours.