A pedestrian path-planning model in accordance with obstacle's danger with reinforcement learning
This work addresses pedestrian simulation for urban planning or safety applications, but it is incremental as it builds on existing reinforcement learning methods for path planning.
The paper tackled pedestrian navigation by proposing a reinforcement learning model that incorporates human perception of danger and environment, resulting in paths that closely resemble human walking conventions and behaviors.
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.