  Current research on pedestrian behavior understanding focuses on the dynamics
of pedestrians and makes strong assumptions about their perceptual abilities.
For instance, it is often presumed that pedestrians have omnidirectional view
of the scene around them. In practice, human visual system has a number of
limitations, such as restricted field of view (FoV) and range of sensing, which
consequently affect decision-making and overall behavior of the pedestrians. By
including explicit modeling of pedestrian perception, we can better understand
its effect on their decision-making. To this end, we propose an agent-based
pedestrian behavior model Intend-Wait-Perceive-Cross with three novel elements:
field of vision, working memory, and scanning strategy, all motivated by
findings from behavioral literature. Through extensive experimentation we
investigate the effects of perceptual limitations on safe crossing decisions
and demonstrate how they contribute to detectable changes in pedestrian
behaviors.
