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

在杂乱的环境中安全导航需要避开静止和移动的障碍物，更具体地说，是避开其他行人。幸运的是，行人或其他物体之间很少发生碰撞。这是因为视觉在安全和有效地引导我们的路线远离碰撞和走向开阔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，允许个人对障碍做出适当的反应(例如，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，引导他们走向洞口，远离会造成伤害的建筑物。然而，特定的光学变量(来自视觉)被用来控制动作并安全地避免与环境物体碰撞的方式是未知的。从根本上说，静止物体和移动物体之间以及人类障碍和非人类障碍之间存在差异。因此，了解躲避他人与非人类物体(静止或移动)时的动作有何不同，这一点至关重要。我的基础研究计划的长期目标是制定系统的规则，管理一个人在不同环境中与其他人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，评估障碍的特定人(即大小)和特定情况(移动或静止)的回避行为特征。这些研究将涉及真实世界和虚拟现实环境，参与者将沿着一条通往目标的道路行走，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，以反映何时何地收集视觉信息；以及2)参与者的运动学(身体动作)，以反映他们的回避行为。这项工作将确定有关障碍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。这最终将导致理解控制杂乱环境中移动的行为变量，这些变量可用于开发机器人，使其安全地在陌生环境中导航，或适当地规划建筑物中的出口路线。这种类型的信息将有助于扩展许多行业(电影、安全、建筑)中使用的人群模拟模型，以准确模拟自然环境中的人与人之间的交互。

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.