Estimation of Driving Resistance Coefficients with Neural Network and Its Low-Power Implementation using FPGA
ニューラルネットワークを用いた走行抵抗係数推定とそのFPGAによる低消費電力実装
- Delivery
- Available on the other site
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- Publication code
- 20224227
- Paper/Info type
- JSAE Transaction
Vol.53 No.3
- Pages
- 605-610(Total 6 p)
- Date of publication
- May 2022
- Publisher
- JSAE
- Language
- Japanese
Detailed Information
| Category(J) | 研究論文 Translation |
|---|---|
| Category(E) | ResearchPaper |
| Author(J) | 1) 花俣 槙一, 2) 穐山 空道, 3) 平田 光男 |
| Author(E) | 1) Shinichi Hanamata, 2) Soramichi Akiyama, 3) Mitsuo Hirata |
| Affiliation(J) | 1) 宇都宮大学, 2) 東京大学, 3) 宇都宮大学 |
| Abstract(J) | カーボンニュートラル実現のため試験環境のみならず実際の一般路での自動車の状態取得が必要である．しかしこれは必要なセンサー設置の手間・費用から難しい．そこで本研究では一般路走行中の走行抵抗係数を直接計測によらずニューラルネットワークで推定し，提案モデルを FPGA に実装し車載動作を可能にする． Translation |
| Abstract(E) | It is important to acquire the states of a car not only on test environments but also on real roads to realize a carbonneutral society. However, it is labor- and cost- inefficient due to the need of many in-car sensors. To alleviate this issue, this research acquires driving resistance coefficients by not directly measuring them but by predicting them using a neural network. In addition, we implement the proposed neural network model on an FPGA to enable its execution within the spare power of a car. |
