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

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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.