DA-IVE: MLP Based Data Association Method for Instantaneous Velocity Estimation Using Multi-Radar: An Experimental Validation Study
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- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2021-01-0092
- 掲載ページ
- 1-11(Total 11 p)
- 発行年月
- 2021年 4月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- SAE WCX Digital Summit 2021
書誌事項
| 著者（英） | 1) Bahareh Shakibajahromi, 2) Anirudh Sarathy Krishnan, 3) Dilip Ati, 4) Amirhossein Jabalameli, 5) Steven Kanzler, 6) Saeed Shayestehmanesh |
|---|---|
| 勤務先（英） | 1) ZF North America Inc., 2) ZF North America Inc., 3) ZF North America Inc., 4) ZF North America Inc., 5) ZF North America Inc., 6) ZF North America Inc. |
| 抄録（英） | This paper describes a novel Multi-Layer Perceptrons (MLP) learning-based association algorithm that is used in conjunction with an Instantaneous Velocity Estimator (IVE) to estimate the velocity of a surrounding vehicle using multi-radar sensors. The IVE algorithm requires at least two targets to be able to provide a velocity estimate. The approach suggested in this paper performs three stages of filtering on a list of targets available for the association to a given track. The algorithm identifies the one pair of targets that will provide the best instantaneous velocity estimation from all possible pairs. The three stages of filtering described ahead are, I - Semantic gating, II - MLP scoring, and III - Algebraic scoring. The IVE algorithm performs linear regression on the pair of targets it is finally provided to come up with a velocity estimation. This research also describes a novel method of labeling radar targets for use in the training of the neural network in association stage II. A thorough analysis of the correlation between a radar target’s quality and attributes is performed and presented here. The performance of the proposed algorithm is evaluated using real-world data collected through the ZF Automated Driving prototype vehicle. 翻訳 |
