VeMo: 車両ダイナミクスをモデル化する軽量データ駆動アプローチ
VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics
車両の過去の状態と操作から将来の状態を予測するGRUベースのエンコーダ・デコーダモデルを提案し、極端な動的条件下でも平均相対誤差2.6%以下を達成した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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6. 次に読むべき論文は？
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著者: Girolamo Oddo, Roberto Nuca, Matteo Parsani
分類: cs.RO, cs.LG, math.DS
原文アブストラクト
Developing a dynamic model for a high-performance vehicle is a complex problem that requires extensive structural information about the system under analysis. This information is often unavailable to those who did not design the vehicle and represents a typical issue in autonomous driving applications, which are frequently developed on top of existing vehicles; therefore, vehicle models are developed under conditions of information scarcity. This paper proposes a lightweight encoder-decoder model based on Gate Recurrent Unit layers to correlate the vehicle's future state with its past states, measured onboard, and control actions the driver performs. The results demonstrate that the model achieves a maximum mean relative error below 2.6% in extreme dynamic conditions. It also shows good robustness when subject to noisy input data across the interested frequency components. Furthermore, being entirely data-driven and free from physical constraints, the model exhibits physical consistency in the output signals, such as longitudinal and lateral accelerations, yaw rate, and the vehicle's longitudinal velocity.
