Quantification of the effect of car cabin design on mental workload during driving
| Project/Area Number |
18K03898
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 18030:Design engineering-related
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| Research Institution | Kanazawa University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 認知負担 / 自動車運転 / 眼球運動計測 / 機械学習 / 異常検知 / ドライビングシミュレータ / 人間工学 / メンタルワークロード / 眼球運動 / 運転余裕 / 設計工学 / バーチャルリアリティ / ヒューマンインタフェース |
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| Outline of Final Research Achievements |
This study aimed to quantify the mental workload of driving a car from eye movement measurement data, and to evaluate the influence of cabin space construction on mental workload. We conducted an experiment in which a driving task using a driving simulator and a secondary task to control the mental workload were imposed simultaneously, and we extracted eye movement parameters useful for evaluating the mental workload. We also proposed a method to quantify mental workload by applying anomaly detection in a machine learning framework. We then applied the proposed method to evaluate the effect of cabin space height on mental workload.
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| Academic Significance and Societal Importance of the Research Achievements |
交通事故を防止する上で，運転者の認知負担を定量化することは重要な課題である．認知負担は直接計測することができないが，機械学習における異常検知を応用して認知負担を定量化する手法を提案したことが，本研究の学術的意義である．また，車室空間の高さが認知負担に与える影響を評価したことは，提案手法が実際の設計に適用可能であることを示すものであり，社会的に有益であると考えられる．
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Report
(4 results)
Research Products
(14 results)
