アンセンテッドカルマンフィルタによる先行車の減速意図予測モデルの構築と検証
Development and Evaluation of Deceleration Intent Inference System by Unscented Kalman Filter
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- 他サイトにて提供・販売
- 入手方法の確認はこちら
- 文献番号
- 20214146
- 文献・情報種別
- 自動車技術会論文集
Vol.52 No.2
- 掲載ページ
- 343-348(Total 6 p)
- 発行年月
- 2021年 3月
- 出版社
- (公社)自動車技術会
- 言語
- 日本語
書誌事項
| カテゴリ | 研究論文 |
|---|---|
| カテゴリ（英） | ResearchPaper 翻訳 |
| 著者 | 1) 若林 翔, 2) 鈴木 宏典 |
| 著者（英） | 1) Sho Wakabayashi, 2) Hironori Suzuki |
| 勤務先 | 1) 日本工業大学大学院, 2) 日本工業大学 |
| 抄録 | 先行車の減速を事前に認知できれば，追突リスクの低減が期待できるとの考えの元，本研究では，車両追従モデルを組み込んだアンセンテットカルマンフィルタを用いて車両状態と減速意図の両方を同時に予測するモデルを提案した．数値計算の結果，従来の手法に比べて，高い精度で減速意図が予測できることを確認した． |
| 抄録（英） | The working brake lights are only the way to recognize the deceleration of preceding vehicle even if the vehicle is equipped with a highly developed autonomous driving system. The prior recognition of deceleration intent would cause a mitigation of a risk of rear-end collision especially in a high-density and high-speed car-following state. The authors have been developing a system that infers deceleration intention 1.5 s in advance of its driver’s most likely action. However, the previous system that utilizes an unscented Kalman filter (UKF) consists of two isolated processes, a state estimation of vehicle platooning and a prediction of deceleration intention. This separation causes an over- or under-estimate of the intent to worsen the prediction precision. This paper aims to improve the intent inference system by combining both processes within a single procedure. In addition, artificial neural network model and multiple regression model are introduced to estimate both vehicle state variables together with the inferred intention. Numerical analyses showed that the revised model provided more accurate intention compared to the previous system for all five participants. It can be concluded that the integrated state feedback system is appropriately working not only for the state estimation but also the prediction of deceleration intent inference. 翻訳 |
