When two pedestrians approach each other on the sidewalk head-on, they sometimes engage in an awkward interaction, both deviating to the same side (repeatedly) to avoid a collision. This phenomenon is known as the sidewalk salsa. Although well known, no existing model describes how this "dance" arises. Such a model must capture the nuances of individual interactions between pedestrians that lead to the sidewalk salsa. Therefore, it could be helpful in the development of mobile robots that frequently participate in such individual interactions, for example, by informing robots in their decision-making. Here, I present a model based on the communication-enabled interaction framework capable of reproducing the sidewalk salsa. The model assumes pedestrians have a deterministic plan for their future movements and a probabilistic belief about the movements of another pedestrian. Combined, the plan and belief result in a perceived risk that pedestrians try to keep below a personal threshold. In simulations of this model, the sidewalk salsa occurs in a symmetrical scenario. At the same time, it shows behavior comparable to observed real-world pedestrian behavior in scenarios with initial position offsets or risk threshold differences. Two other scenarios provide support for a hypothesis from previous literature stating that cultural norms, in the form of a biased belief about on which side others will pass (i.e. deviating to the left or right), contribute to the occurrence of the sidewalk salsa. Thereby, the proposed model provides insight into how the sidewalk salsa arises.

翻译：当两名行人迎面在人行道上相遇时，他们有时会陷入一种尴尬的互动，双方为避让碰撞而（反复地）偏向同一侧。这种现象被称为“人行道萨尔萨”。尽管广为人知，但现有模型均未描述这种“舞蹈”是如何产生的。此类模型必须捕捉导致人行道萨尔萨现象的行人间个体互动的细微差别。因此，它可能有助于开发经常参与此类个体互动的移动机器人，例如通过为机器人的决策提供信息。本文提出了一种基于通信交互框架的模型，该模型能够复现人行道萨尔萨现象。该模型假设行人对其未来运动具有确定性规划，并对另一名行人的运动具有概率性信念。规划与信念相结合产生感知风险，行人会试图将风险维持在个人阈值以下。在该模型的仿真中，人行道萨尔萨现象出现在对称场景中。同时，在具有初始位置偏移或风险阈值差异的场景中，模型表现出了与观测到的真实世界行人行为相当的一致性。另外两个场景为先前文献中的假设提供了支持，即文化规范（表现为对他人将从哪侧通过——即向左或向右偏让——的偏向性信念）促成了人行道萨尔萨现象的发生。由此，所提出的模型揭示了人行道萨尔萨现象的产生机制。