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态和移动的障碍物，更具体地说，需要避开其他行人。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睛运动来收集的，从而允许个体适当地行动（即，改变速度、方向或脚的位置）。通过这种方式，视觉可以帮助行人选择路径，引导他们走向开口，远离可能造成伤害的结构。然而，具体的光学变量（从视觉）用于控制行动和安全地避免与环境物体碰撞的方式是未知的。从根本上讲，静止和移动物体之间以及人类和非人类障碍物之间存在差异。因此，了解与非人类物体（静止或移动）相比，避开另一个人时的动作有何不同至关重要。我的基础研究计划的长期目标是开发系统的规则，管理个人的行为时，与其他个人和对象在不同的环境中。为了实现这一目标，一系列评估障碍的个人特异性（即，在未来五年内，我们会进行一项研究，以评估不同环境（移动或静止）对回避行为的影响。这些研究将涉及真实世界和虚拟现实环境，参与者将沿着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，以及真实的或虚拟的障碍物碰撞。障碍物将直接沿着参与者的行走路径定位，并且在任何给定的试验中，障碍物可以朝向参与者移动或保持静止。收集的数据将包括：1）注视行为，以反映在何处以及何时收集视觉信息;以及2）参与者的运动学（身体运动），以反映他们的回避行为。这项工作将确定有关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，这些变量可用于开发机器人，以安全地在不熟悉的环境中导航或正确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.