Collision avoidance behaviours of people interacting with other people and objects within different environments

在混乱的环境中安全导航需要避开静态和移动的障碍物，更具体地说，要避开其他行人。幸运的是，行人或其他物体之间的碰撞很少发生。这是由于视觉在安全有效地引导我们的路线远离碰撞并走向开放空间方面发挥了关键作用。关于环境的视觉信息是通过眼球运动收集的，允许个体对障碍物做出适当的反应（即改变速度、方向或脚的位置）。通过这种方式，视觉帮助行人选择路径，引导他们走向开口，远离可能造成伤害的建筑物。然而，使用特定的光学变量（来自视觉）来控制动作并安全避免与环境物体碰撞的方式尚不清楚。从根本上说，静止和移动的物体之间以及人类和非人类障碍物之间存在差异。因此，了解在避开另一个人和非人类物体（静止或移动）时的行为差异是至关重要的。我的基础研究项目的长期目标是发展出系统的规则来管理个体在不同环境中与其他个体和物体互动时的行为。为了实现这一目标，将在未来五年内进行一系列研究，评估障碍物的个人特异性（即大小）和情境特异性（移动或静止）对回避行为的特征。这些研究将涉及现实世界和虚拟现实环境，在这些环境中，参与者将沿着一条通往目标的路径行走，避免与人类或非人类、移动或静止、真实或虚拟的障碍物相撞。障碍物将直接位于参与者的行走路径上，在任何给定的试验中，障碍物可以向参与者移动或保持静止。收集的数据将包括：1)凝视行为，以反映收集视觉信息的地点和时间；2)参与者的运动学（身体运动）来反映他们的回避行为。这项工作将确定关于障碍物特征的光学信息如何影响回避行为，以及这些行为是否会随着寿命或运动训练而改变。这将最终导致理解在混乱环境中控制运动的行为变量，这可以用来开发机器人安全地在不熟悉的环境中导航，或者在建筑物中正确规划出口路线。这种类型的信息将有助于扩展在许多行业（电影、安全、建筑）中使用的人群模拟模型，以准确地模拟自然环境中的人与人之间的互动。

Navigating safely through a cluttered environment requires avoiding static and moving obstacles and, more specifically, other pedestrians. Fortunately, collisions between pedestrians or other objects rarely occur. This is due to a key role that vision plays in safely and efficiently guiding our routes away from collisions and towards open spaces. Visual information about an environment is gathered through eye movements allowing individuals to act appropriately (i.e., change speed, direction, or foot placement) in response to obstacles. In this way vision helps pedestrians to select pathways that will guide them towards openings and away from structures that would cause injury. However, the manner in which specific optical variables (from vision) are used to control actions and safely avoid collisions with environmental objects are unknown. Fundamentally there are differences between stationary and moving objects as well as between human and non-human obstacles. Therefore, it is critical to understand how actions differ when avoiding another person compared to a non-human object (stationary or moving). The long-term goal of my basic research program is to develop systematic rules that govern an individual's actions when interacting with other individuals and objects in different environments. In order to meet this objective, a series of studies that assess an obstacle's person-specific (i.e., size) and situation-specific (moving or stationary) characteristics on avoidance behaviours will be conducted over the next five years. These studies will involve real-world and virtual reality environments in which participants will walk along a path toward a goal and avoid colliding with obstacles that are human or non-human, moving or stationary, and real or virtual. The obstacles will be located directly along a participant's walking path and on any given trial the obstacles can move towards the participants or remain stationary. Data collected will include: 1) gaze behaviours to reflect where and when visual information is being gathered; and 2) kinematics (body movements) of the participants to reflect their avoidance behaviours. This work will determine how optical information about an obstacle's characteristics affects avoidance behaviours and whether these behaviours change over the lifespan or with athletic training. This will ultimately lead to understanding the behavioural variables that control locomotion in cluttered environments, which can be used to develop robots to safely navigate unfamiliar environments or properly plan exit routes in buildings. This type of information will help expand crowd simulation models used in many industries (film, security, architecture) to accurately simulate person-person interactions in natural environments.