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Summary & Details

Construction of an Accelerator Operation Prediction Model Based on Driving Data Clustering Considering Driving Characteristics

運転特性を考慮した走行データクラスタリングに基づくアクセル操作予測モデルの構築

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Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20266141

Paper/Info type: Proceedings (Autumn)

No.121-26

Pages: 1-2(Total 2 p)

Date of publication: Oct 2026

Publisher: JSAE

Language: Japanese

Event: 2026 JSAE Annual Congress (Autumn)

Detailed Information

Author(J)	1) 細木貴文, 2) 室谷立, 3) 佐藤進
Author(E)	1) Takafumi Hosogi, 2) Ryu Murotani, 3) Susumu Sato
Affiliation(J)	1) 東京科学大学, 2) 東京科学大学, 3) 東京科学大学
Affiliation(E)	1) Institute of Science Tokyo, 2) Institute of Science Tokyo, 3) Institute of Science Tokyo
Abstract(J)	本研究では，実路走行データを運転特性に基づいてクラスタリングし，クラスタごとにLSTMベースのアクセル操作予測モデルを構築した．提案手法は走行データごとの操作傾向を反映した予測を行うことを目的としている．実験結果より，クラスタ別にモデル化することで，アクセル操作の予測性能が向上することが示された． Translation
Abstract(E)	In this study, real-world driving data were clustered based on driving characteristics, and an LSTM-based accelerator operation prediction model was constructed for each cluster. The proposed method aims to perform prediction that reflects the operational tendencies of each driving dataset. The experimental results showed that cluster-specific modeling improved the prediction performance of accelerator operation.

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