Context-Sensitive Driver Model for Determining Recommended Speed in Intersection Driving Scenarios
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20219046
- Paper/Info type
- Other International Conferences
- Pages
- 1-6(Total 6 p)
- Date of publication
- Sep 2021
- Publisher
- JSAE
- Language
- English
Detailed Information
| Author(E) | 1) Yuichi Saito, 2) Fumio Sugaya, 3) Shintaro Inoue, 4) Pongsathorn Raksincharoensak, 5) Hideo Inoue |
|---|---|
| Affiliation(E) | 1) University of Tsukuba, 2) Toyota Motor Corp., 3) Toyota Motor Corp., 4) Tokyo University of Agriculture and Technology, 5) Kanagawa Institute of Technology |
| Abstract(E) | Near-miss events or accidents involved pedestrians and cyclists are the result of conflict between a driver behavior and the road user behavior. When expert drivers with more driving experience are facing uncertainty, they naturally seek to reduce the uncertainty by attempting to fit their current driving context into a pre-existing category based on knowledge-based decision making. Our study goal is to develop DAS to attain “a hazard-anticipatory driving” depending on driving contexts, through both the driver behavior analysis and the clarification of cause-and-effect chain in accident mechanism. The near-miss incident database has been constructed and managed by the Smart Mobility Research Center (SMRC [3]) of Tokyo University of Agriculture and Technology in JAPAN since 2004. The main contribution of this paper is to propose a context-sensitive driver model to determine the recommended speed in intersection scenarios. Based on investigations with the near-miss event database, this paper describes a method for determining the recommended safe speed based on the annotated information by applying the statistical processing and the machine learning techniques, and then this study explores the mechanism of adjusting the vehicle velocity according to the given road environment context. |
