自動運転車の強化されたGDMとRLSを用いたリアルタイムタイヤの硬さの推定
Zhenyu Qin1, Jiaqi Wang2, Panxue Liu3
1School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian, China.
Scientific reports
|August 29, 2025
まとめ
タイヤの側面の硬さ (TLS) の正確な推定は,自動運転車の安全性にとって不可欠です. ディープラーニングにインスパイアされたノベル・グラデント・デッサント・メソッド (GDM) は,TLSのリアルタイム・トラッキングを向上させ,車両の制御を強化します.
科学分野:
- 車両ダイナミクスと制御
- エンジニアリングアプリケーションのための機械学習
- 自動車 安全 システム
背景:
- タイヤと道路の非線形相互作用によって大きく影響を受ける.
- タイヤの横向きの硬さ (TLS) は,タイヤと道路の状況によって変化する,ゆらぎの安定性に影響を与える重要なパラメータです.
- TLSの正確な推定は,特に厳しいシナリオでは,自動運転の安全性にとって極めて重要です.
研究 の 目的:
- タイヤの横の硬さ (TLS) の識別のための新しい枠組みを提案する.
- TLSの見積もりのためのディープラーニングにインスパイアされた修正されたグラデントダウンメソッド (GDM) を探求する.
- TLSトラッキングのための改善されたリアルタイムアルゴリズムを開発し,評価する.
主な方法:
- 再帰最小二乗 (RLS) と GDM の間の理論的なリンクを確立し,RLS を特定の GDM ケースとして特定しました.
- リアルタイムTLS識別のための改良されたRLS変種を開発しました.
- Adamのような適応的方法を含む様々な条件下で様々なGDMとRLSアルゴリズムを比較するためにシミュレーションを使用しました.
主要な成果:
- 異なるアルゴリズムと異なる条件で有効なTLS追跡能力を実証した.
- アダムのようなアダプティブGDM方法は,TLSを追跡する上で優れたパフォーマンスを示しました.
- アダプティブ・メソッドで5%未満の相対安定状態エラー (RSSE) と3秒未満の応答時間 (t10) を達成した.
結論:
- 提案されたGDMにインスパイアされたフレームワークは,TLSをリアルタイムで効果的に識別します.
- アダムのようなアダプティブアルゴリズムは 自動運転車におけるTLS推定の性能を向上させます
- 結果は,安全性が重要な自動運転システムの適切な推定器を選択するための実用的な洞察を提供します.
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