Driving Risk Prediction Over a Repetitive Driving Pattern
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20265330
- Paper/Info type
- Proceedings (Spring)
No.76-26
- Pages
- 1-5(Total 5 p)
- Date of publication
- May 2026
- Publisher
- JSAE
- Language
- English
- Event
- 2026 JSAE Annual Congress (Spring)
Detailed Information
| Author(J) | 1) Michele Guagnano, 2) Yecan Wang, 3) Shigenobu Mitsuzawa, 4) Massimo Violante, 5) Riccardo Groppo |
|---|---|
| Author(E) | 1) Michele Guagnano, 2) Yecan Wang, 3) Shigenobu Mitsuzawa, 4) Massimo Violante, 5) Riccardo Groppo |
| Affiliation(J) | 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 |
| Affiliation(E) | 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 |
| Abstract(E) | 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%. |
