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.

翻译：当前关于行人行为理解的研究主要关注行人动态，并对其感知能力做出了强假设。例如，通常假定行人对周围场景拥有全向视野。在实际中，人类视觉系统存在诸多局限，例如受限的视场和传感范围，这些因此影响着行人的决策制定及整体行为。通过显式建模行人感知，我们能更好地理解其对行人决策的影响。为此，我们提出了一种基于智能体的行人行为模型——意图-等待-感知-过街（Intend-Wait-Perceive-Cross），该模型包含三个受行为学文献启发的创新要素：视野、工作记忆及扫描策略。通过大量实验，我们研究了感知局限对安全过街决策的影响，并展示了这些局限如何导致行人行为的可检测变化。