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    Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

    Source: ASME Letters in Dynamic Systems and Control:;2024:;volume( 005 ):;issue: 001::page 11004-1
    Author:
    Mai, Rene E.
    ,
    Sears, Katherine
    ,
    Roessling, Grace
    ,
    Julius, Agung
    ,
    Mishra, Sandipan
    DOI: 10.1115/1.4066630
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle’s lateral state.
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      Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306400
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    contributor authorMai, Rene E.
    contributor authorSears, Katherine
    contributor authorRoessling, Grace
    contributor authorJulius, Agung
    contributor authorMishra, Sandipan
    date accessioned2025-04-21T10:32:18Z
    date available2025-04-21T10:32:18Z
    date copyright10/11/2024 12:00:00 AM
    date issued2024
    identifier issn2689-6117
    identifier otheraldsc_5_1_011004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306400
    description abstractWe derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle’s lateral state.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleGeneralized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1
    typeJournal Paper
    journal volume5
    journal issue1
    journal titleASME Letters in Dynamic Systems and Control
    identifier doi10.1115/1.4066630
    journal fristpage11004-1
    journal lastpage11004-7
    page7
    treeASME Letters in Dynamic Systems and Control:;2024:;volume( 005 ):;issue: 001
    contenttypeFulltext
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