Driving Risk Prediction Over a Repetitive Driving Pattern
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- 形態
- 価格
- 一般価格(税込):¥1,100 会員価格(税込):¥880
- 文献番号
- 20265330
- 文献・情報種別
- 学術講演会予稿集(春)
No.76-26
- 掲載ページ
- 1-5(Total 5 p)
- 発行年月
- 2026年 5月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
- イベント
- 2026年春季大会
書誌事項
| 著者 | 1) Michele Guagnano, 2) Yecan Wang, 3) Shigenobu Mitsuzawa, 4) Massimo Violante, 5) Riccardo Groppo |
|---|---|
| 著者(英) | 1) Michele Guagnano, 2) Yecan Wang, 3) Shigenobu Mitsuzawa, 4) Massimo Violante, 5) Riccardo Groppo |
| 勤務先 | 1) Politecnico di Torino, 2) Honda Motor R&D, 3) Honda Motor R&D, 4) Politecnico di Torino/Sleep Advice Technologies, 5) Sleep Advice Technologies |
| 勤務先(英) | 1) Politecnico di Torino, 2) Honda Motor R&D, 3) Honda Motor R&D, 4) Politecnico di Torino/Sleep Advice Technologies, 5) Sleep Advice Technologies |
| 抄録(英) | Road traffic accidents remain a major global issue, yet professional drivers face a distinct and often underestimated risk: long-term exposure to repetitive routes. To address this, we conducted a longitudinal study with 54 participants over a 12-day protocol, performing 4 consecutive laps during each day on a simulator on a repetitive track to replicate routine shifts. The resulting dataset fuses driving, eye-tracking metrics, and physiological features. An ML model was trained to predict the risk level of a lap with data from the previous one. The model was validated with a K-Fold approach, by achieving a mean accuracy of 91%. 翻訳 |