| Project/Area Number |
17H03326
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Civil engineering project/Traffic engineering
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| Research Institution | Kochi University of Technology |
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Principal Investigator |
Park Kaechang 高知工科大学, 地域連携機構, 客員教授 (60333514)
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| Co-Investigator(Kenkyū-buntansha) |
村井 俊哉 京都大学, 医学研究科, 教授 (30335286)
中川 善典 高知工科大学, 経済・マネジメント学群, 准教授 (90401140)
繁桝 博昭 高知工科大学, 情報学群, 教授 (90447855)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥17,030,000 (Direct Cost: ¥13,100,000、Indirect Cost: ¥3,930,000)
Fiscal Year 2019: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2018: ¥7,280,000 (Direct Cost: ¥5,600,000、Indirect Cost: ¥1,680,000)
Fiscal Year 2017: ¥7,150,000 (Direct Cost: ¥5,500,000、Indirect Cost: ¥1,650,000)
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| Keywords | 高齢者 / 脳 / MRI / 危険運転 / パーソナリティ / 危険運転行動 / ドライビングシミレータ / 脳MRI / 危険運転場面 / 高齢ドライバー / 自動運転 / ドライビングシミュレータ / 仮想現実 / 危険運転回避 / ３D映像 / 咄嗟の危険運転場面 / VR / AR / 危険場面回避 |
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| Outline of Final Research Achievements |
Japan, where the aging rate exceeds 28%, has been the fastest aging country in the world. The measures to prevent dangerous driving of elderly drivers become an urgent issue, so that the driving characteristics of drivers must be further taken into consideration. Targeting Brain Dock patients, three types of dangerous driving behaviors (illegal, irritable, aggressive) were classified and indicated brain features of regional gray matter volumes in each. Furthermore, the aging brain was defined as the grading of white matter lesions and the degree of brain atrophy, and the elderly drover’s operational performance of actual vehicles decreased according to the aging brain. Thus, MRI may enable to identify a dangerous driver in the elderly. In addition, a head-mounted display (HMD) presented 3D imaging of dangerous driving scenes. However, the measures for 3D imaging sickness is indispensable for the prevention from traffic accidents under automated driving system.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果により、運転の個人差を克服することが可能になり、MRI定量データから危険運転挙動のメカニズム解明に繋がる発展性があり、交通工学と脳・精神医科学の融合が推進される。自動運転条件下の免許証発行に関する客観的根拠として活用することで、客観的精度と再現性が確保され、科学的な世界標準モデルとしての自動運転下の危険運転・交通事故防止対策を提案できる。
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