Proposal of a Data-Driven Weakly Supervised Learning Method for Operation Mode Classification of Fuel Cell Garbage Trucks
燃料電池ごみ収集車の運行モード分類におけるデータ駆動型弱教師学習手法の提案
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20265247
- Paper/Info type
- Proceedings (Spring)
No.58-26
- Pages
- 1-6(Total 6 p)
- Date of publication
- May 2026
- Publisher
- JSAE
- Language
- Japanese
- Event
- 2026 JSAE Annual Congress (Spring)
Detailed Information
| Author(J) | 1) 鮑 義達, 2) 張 翔, 3) 方 亦园, 4) 楊 イ翔, 5) 紙屋 雄史 |
|---|---|
| Author(E) | 1) Yida Bao, 2) Xiang Zhang, 3) Yiyuan Fang, 4) Wei-Hsiang Yang, 5) Yushi Kamiya |
| Affiliation(J) | 1) 早稲田大学, 2) 早稲田大学, 3) 早稲田大学, 4) 早稲田大学, 5) 早稲田大学 |
| Affiliation(E) | 1) Waseda University, 2) Waseda University, 3) Waseda University, 4) Waseda University, 5) Waseda University |
| Abstract(J) | 本研究はFCごみ収集車の運行モード分類と統計分析に向け，知見を融合した弱教師深層学習を提案する．学習データの自動生成によりアノテーションコストを75%削減しつつ高精度な分類を実現した．本手法は膨大な実走行CANデータの効率的解析を可能にし，次世代商用車の設計・制御策に寄与する基盤技術になり得ると考える． Translation |
| Abstract(E) | This study proposes a versatile weakly supervised deep learning framework for operating mode classification, which enables statistical feature analysis of Fuel Cell (FC) refuse trucks. By fusing unsupervised clustering of CAN signals with expert knowledge, the method automatically generates large-scale training data, eliminating the need for expensive manual annotation. The proposed model achieves high classification accuracy while reducing annotation costs by over 75%. This framework enables efficient analysis for massive real-world driving data, establishing a foundational technology that has an ability to contribute to the broader design and control strategies of next-generation commercial vehicles. |
