Method of Improving Machine Learning Model for Speed Decision on Community Roads by Extracting Potentially Dangerous Spots
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- Publication code
- 20264070
- Paper/Info type
- International Journal of Automotive Engineering
Vol.17 No.1
- Pages
- 28-42(Total 15 p)
- Date of publication
- Jan 2026
- Publisher
- JSAE
- Language
- English
Detailed Information
| Category(E) | Research paper |
|---|---|
| Author(E) | 1) Yuki Sadanaka, 2) Keita Hori, 3) Takuma Ito |
| Affiliation(E) | 1) The University of Tokyo, 2) The University of Tokyo, 3) The University of Tokyo |
| Abstract(E) | Since traffic accidents on Japanese community roads are one of unsolved problems, we focus on a speed decision machine learning model (SDMLM) targeting community roads. On community roads, occlusions caused by housing walls make invisible risks of collisions at some spots, which we call “potentially dangerous spots.” Although SDMLM needs to output a safe speed at the potentially dangerous spots, SDMLM that does not learn from driving data including occlusionrelated risk might be unable to calculate a suitable speed. Thus, we develop a method for improving the SDMLM by utilizing careful driving data. In the improvement, the trade-off between “adaptation to occlusion-related risk” and “prevention of overcautiousness” needs to be considered. Therefore, for training an improved SDMLM, we extract potentially dangerous spots and prepare careful driving data at extracted spots. Through evaluations using the driving data in the real world, we confirm the effectiveness of the proposed method for improving the SDMLM on community roads. The results of this study will contribute to the development of the component of autonomous driving systems for preventing traffic accidents on Japanese community roads. |
