Cognitive Path Planning With Spatial Memory Distortion

Dubey, Rohit K.; Sohn, Samuel S.; Thrash, Tyler; Hölscher, Christoph; Kapadia, Mubbasir; Borrmann, André

doi:10.1109/tvcg.2022.3163794

Cited by 1 publication

(2 citation statements)

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“…Path-Edit Distance: measures the difference between two paths by counting the minimum number of operations required to transform one path into the other [Dubey et al 2022]. For each wayfinding task (origin-destination pair), we record the route choice at each decision point as a string (e.g., “LFRL” for a path that traverses four decision points and elects one of the following routes.…”

Section: Simulation Results and Validationmentioning

confidence: 99%

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An Evidence-based Cognitive Model of Uncertainty during Indoor Multi-level Human Wayfinding

Yang

Dubey

Kalantari

2022

Preprint

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Wayfinding - the process of goal-directed planned movement in an unfamiliar, large, and complex environment is challenging and often executed under uncertainty. Wayfinding uncertainty is a mental state experienced, especially in an unknown environment, when deciding between two or more competing route choices at a decision point. Even though uncertainty is intrinsic and plays a crucial role during wayfinding, the existing computational model of human wayfinding provides no or minimal support for modeling uncertainty during wayfinding. Therefore, it is paramount to incorporate uncertainty into a wayfinding model to produce realistic human wayfinding. In this paper, we ground the wayfinding process on the concept of oriented search (as proposed by Allen (1999)), employing directional information from signage and spatial layout. We model the two most common and prevalent uncertainty during wayfinding: (1) Route-choice uncertainty: Originates when an occupant is at a decision point and has to select a route out of the multiple route choices. (2) Affirm on-route uncertainty: Originates when an occupant is in-between decision points (e.g., a long corridor, large open space) and tries to ascertain if its current location is on the correct route towards the destination. We model route-choice uncertainty as a function of two attributes: the number of possible outcomes and the probability distribution of belief in electing each outcome at a decision point. We conducted a real-world experiment with XX participants to parameterize and validate the wayfinding uncertainty model. After preliminary analysis of the collected and simulated data, we observe similar wayfinding behavior between simulated agents and participants regarding perceived continuous uncertainty during wayfinding and route-choice behavior at decision points.

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Section: Simulation Results and Validationmentioning

confidence: 99%

“…Average Path Distance: measures the average path distance per wayfinding task for all trajectories [Dubey et al 2022; Huang et al 2017; Madl et al 2015].…”

Section: Simulation Results and Validationmentioning

confidence: 99%