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Busy beeway: a game for testing human-automation collaboration for navigation

Published: 08 November 2017 Publication History

Abstract

This study presents Busy Beeway, a mobile game platform to investigate human-automation collaboration in dynamic environments. In Busy Beeway, users collaborate with automation to evade stochastically moving obstacles and reach a series of goals, in game levels of increasing difficulty. We are motivated by the need for reliable navigation aids in stochastic, dynamic environments, which are highly relevant for self-driving vehicles, UAVs, underwater and surface vehicles, and other applications. The proposed mobile game platform is agnostic to the particular algorithm underlying the autonomous system, can be used to evaluate both fully autonomous as well as human-in-the-loop systems, and is easily deployable, for large, remote user studies. This last element is key for rigorous study of human factors in navigation aids. Through a small 32--user study, we evaluate preliminary findings regarding the relative efficacy of collaborative and fully autonomous navigation, the relationship between success rate and users' learned trust in the automation (gathered via pre- and post-experiment surveys), and tolerance to error (for decisions made by the automation and by the user). This study validates the feasibility of Busy Beeway as a platform for human subject studies on human-automation collaboration, and suggests directions for future research in human-aided planning in difficult environments.

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    cover image ACM Conferences
    MIG '17: Proceedings of the 10th International Conference on Motion in Games
    November 2017
    128 pages
    ISBN:9781450355414
    DOI:10.1145/3136457
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 08 November 2017

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    Author Tags

    1. collaborative navigation
    2. human-automation interaction
    3. motion planning

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    MiG '17: Motion in Games
    November 8 - 10, 2017
    Barcelona, Spain

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