System Identification Techniques to Improve ADAS Feature Performance
- 提供方法
- 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.2022-01-0091
- 掲載ページ
- 1-10(Total 10 p)
- 発行年月
- 2022年 3月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- WCX SAE World Congress Experience 2022
書誌事項
| 著者（英） | 1) Arpit Awathe, 2) Tejas Varunjikar, 3) Subhabrata Ganguli |
|---|---|
| 勤務先（英） | 1) Magna Electronics Inc., 2) Magna Electronics Inc., 3) Magna Electronics Inc. |
| 抄録（英） | The advances in automotive technology continue to deliver safety and driving comfort benefits to society. The Automated Driving Assistance System (ADAS) technology is at the forefront of this evolution. Today, various vehicle models on the road have features like lane centering, automated emergency braking, adaptive cruise control, traffic jam assist etc. During early development, such feature algorithms often assume ideal environmental and vehicle conditions while doing performance evaluation. It is imperative that one uses realistic scenarios for production development. To demonstrate this, the lane centering ADAS feature performance is studied using a test vehicle. The feature considered here is an end-to-end feature, i.e., from camera sensor output to steering actuation. Lane centering control system often has multiple control loops within the vehicle system. The delay in steering system response has a significant effect on overall lane centering performance and driver feel. This study focuses on understanding dynamics of Electronic Power Steering (EPS) behavior and its overall ADAS feature performance. System identification techniques are used to understand EPS dynamics as well as vehicle lateral dynamics. Furthermore, the plant models identified are used to improve lane centering performance in the vehicle. 翻訳 |
