2000年至2025年自动驾驶汽车的图书计量和可视化分析路径规划和轨迹跟踪研究
1Institute of Machinery, Materials, and Transport, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
专注于路线规划和轨迹跟踪的自动驾驶研究显示出显著的增长. 基于学习的方法,特别是强化学习 (RL) 和深度强化学习 (DRL),对于先进的自动驾驶汽车控制越来越重要.
科学领域:
- 机器人和智能系统 机器人和智能系统
- 汽车工程 汽车工程
- 计算机科学 计算机科学
背景情况:
- 自动驾驶是一个快速发展的领域,其核心组成部分是路径规划和轨迹跟踪.
- 了解研究环境对于未来的发展至关重要.
研究的目的:
- 进行自动驾驶汽车路径规划和轨迹跟踪研究的文献分析和LDA主题建模.
- 确定出版趋势,主要贡献者和主要的研究主题.
主要方法:
- 从科学网络数据库中对出版物的图书法分析.
- 隐性迪里克莱特分配 (LDA) 主题建模以确定研究主题.
- 分析出版趋势,作者引用,机构和关键词的同时出现情况.
主要成果:
- 研究成果的持续增长,轨迹规划,路径优化和轨迹跟踪作为主导主题.
- 基于学习的方法越来越突出,特别是强化学习 (RL) 和深度强化学习 (DRL).
- 中国和美国被确定为该领域的主要贡献者.
结论:
- 该研究提供了对知识结构和自动驾驶研究发展趋势的系统概述.
- 突出了关键的机遇和挑战,支持未来在更安全,更智能的自动驾驶系统方面的进步.
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