A Humanized Vehicle Speed Control to Improve the Acceptance of Automated Longitudinal Control
- 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.2022-01-0095
- 掲載ページ
- 1-12(Total 12 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) Enrico Raffone, 2) Massimo Fossanetti, 3) Claudio Rei cEng |
---|---|
勤務先(英) | 1) Centro Ricerche FIAT ScpA, 2) Centro Ricerche FIAT ScpA, 3) Centro Ricerche FIAT ScpA |
抄録(英) | Vehicle speed controls, as adaptive cruise control and related automated evolutions, are control systems able to follow a desired vehicle reference speed that is set by the driver and fused with information as road signs, SD maps etc.. Current normal production systems don’t distinguish among the vehicle users, only some carmakers are doing first steps towards the introduction of learning from driver to adapt the traditional control. In our work, we follow up this content with a humanized speed control, based on learning of driver longitudinal behavior. This method is able to combine machine learning algorithms, vehicle positioning and recurrent trips into existing automated longitudinal control systems. Proposed algorithm can reduce the interactions between drivers and automated systems by improving the acceptance of automated longitudinal control. Furthermore, proposed integration works mainly on speed reference that dramatically simplifies the customization of the system. We present the general methodology of our online learning procedure and suggest how to integrate proposed work in a normal production vehicle. 翻訳 |