  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.
