Interacting Streams of Cognitive Active Agents in a Three-Way Intersection

TL;DR

This study uses simulations of cognitive active agents to explore how vision angle and maneuverability influence pedestrian flow and collective behavior at a three-way intersection, revealing diverse motion patterns and universal flow characteristics.

Contribution

It introduces a model of cognitive active Brownian particles with directed perception to analyze pedestrian dynamics, highlighting the effects of vision angle and maneuverability on emergent behaviors.

Findings

Different motion regimes depend on vision angle and maneuverability.

A jammed state emerges at large vision angles and high maneuverability.

The fundamental flow diagram is universal across vision angles.

Abstract

The emergent collective motion of active agents - in particular pedestrians - at a three-way intersection is studied by Langevin simulations of cognitive intelligent active Brownian particles (iABPs) with directed visual perception and self-steering avoidance. Depending on the maneuverability $\Omega$, the goal fixation $K$, and the vision angle $\psi$, different types of pedestrian motion emerge. At intermediate relative maneuverability $\Delta = \Omega/K$ and large $\psi$, pedestrians have noisy trajectories due to multiple scattering events as they encounter other pedestrians in their field of view. For $\psi = \pi$ and large relative maneuverability $\Delta$, an effectively jammed state is found, which belongs to the percolation universality class. For small $\psi$, agents exhibit localised clustering and flocking, while for intermediate $\psi$ self-organized rotational flows can emerge. The analysis of mean squared displacement and velocity auto-correlation of the agents reveals that the motion is well described by fractional Brownian Motion with positively correlated noise. Finally, despite the rich variety of collective behaviour, the fundamental flow diagram for the three-way-crossing setup shows a universal curve for the different vision angles. Our research provides valuable insights into the importance of vision angle and self-steering avoidance on pedestrian dynamics in semi-dense crowds.