経路計画と軌道の追跡に関する図書館計測と視覚化分析 2000年から2025年にかけての自動運転車両に関する研究
1Institute of Machinery, Materials, and Transport, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.
Sensors (Basel, Switzerland)
|February 13, 2026
まとめ
路線計画と軌道の追跡に焦点を当てた自律運転の研究は,著しい成長を示しています. 学習ベースの方法,特に強化学習 (RL) と深層強化学習 (DRL) は,高度な自動運転車両制御にとってますます重要になっています.
科学分野:
- ロボット工学とインテリジェント・システム
- 自動車工学 自動車工学
- コンピュータサイエンス コンピュータサイエンス
背景:
- 自動運転は,経路計画と軌道を追跡するコアコンポーネントとして急速に進歩する分野です.
- 研究環境を理解することは,将来の開発に不可欠です.
研究 の 目的:
- 自律走行車の経路計画と軌道の追跡に関する研究について,図書館計測分析とLDAトピックモデリングを実施する.
- 出版の傾向,主要な貢献者,および主要な研究テーマを特定する.
主な方法:
- Web of Scienceデータベースからの出版物の図書館計測分析.
- 研究テーマを特定するためのLatent Dirichlet Allocation (LDA) トピックモデリング.
- 出版の動向,著者の引用,機関,キーワードの同時出現の分析.
主要な成果:
- 研究成果の持続的な成長,軌道の計画,軌道の最適化,軌道の追跡が支配的なトピックとして.
- 学習ベースのアプローチ,特に強化学習 (RL) と深層強化学習 (DRL) の重要性が高まっている.
- 中国と米国は,この分野における主要な貢献者であると特定されました.
結論:
- この研究は,自律運転の研究における知識構造と進化する傾向の体系的な概要を提供します.
- 重要な機会と課題が強調され,より安全でインテリジェントな自動運転システムの将来の進歩を支援しています.
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