Alerting systems are a prevalent and integral part of modem cockpits. When alerting systems serve their intended roles in the cockpit, they can increase safety through monitoring for, and directing pilot attention to, developing hazards. However, numerous studies have identified several problematic types of interactions between pilot and alerting systems. Preventing problematic interactions between pilots and alerting systems requires a methodology that can capture and clarify the extent to which pilots will innately agree with and ultimately rely upon alerting systems. This paper details the development of Human-Automated Judgment Learning (HAJL), a new methodology attempting to provide these capabilities. The first section provides the background with regard to modeling and measuring pilot interaction with alerting systems. The next section presents the HAJL methodology. Then an initial experiment substantiating the HAJL methodology is described.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Human-automated judgment learning: applying interpersonal learning to investigate human interaction with alerting systems


    Contributors:


    Publication date :

    2002-01-01


    Size :

    1033475 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    A Wizard-of-Oz vehicle to investigate human interaction with AI-driven automated cars

    Diederichs, Frederik / Mathis, Lesley-Ann / Bopp-Bertenbreiter, Valeria et al. | DataCite | 2021


    Automated signal compliance monitoring and alerting system

    JORDAN LAWRENCE B / SCHABELL BRANDON / WEAVER BRYAN et al. | European Patent Office | 2023

    Free access

    Automated signal compliance monitoring and alerting system

    JORDAN LAWRENCE B / SCHABELL BRANDON / WEAVER BRYAN et al. | European Patent Office | 2021

    Free access