关于智能驾驶的车辆轨迹预测程序的调查
Tingjing Wang1, Daiquan Xiao2, Xuecai Xu2
1School of Architectural Engineering, Zhejiang College of Construction Technology, Hangzhou 311215, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项调查审查了智能驾驶轨迹预测,涵盖感知,核心预测技术,决策和应用. 它为推进车辆轨迹预测方法和现实世界的实施提供了洞察力.
科学领域:
- 智能交通系统
- 自动驾驶技术
- 计算机视觉
背景情况:
- 预测车辆轨迹对于智能驾驶的安全性和效率至关重要.
- 现有的审查往往缺乏全面的理论和实践视角.
研究的目的:
- 提供智能驾驶中的车辆轨迹预测程序的全面审查.
- 从感知到应用,对当前方法进行分类和分析.
- 确定未来的研究方向.
主要方法:
- 将轨迹预测方法分为短期 (基于物理,机器学习) 和长期 (基于深度学习,意图) 的领域.
- 对感知层技术 (传感器,视觉,多式融合) 的分析.
- 审查决策层 (合作,闭环,实时优化) 和场景应用 (开放,关闭).
主要成果:
- 在领先的智能车辆中使用的传感器和感知技术的详细列举.
- 核心轨迹预测算法和决策策略的总结.
- 讨论开放式和封闭式场景应用及其实际考虑.
结论:
- 这项研究综合了目前关于智能驾驶的车辆轨迹预测的知识.
- 它强调了关键的技术和对感知,预测和决策的挑战.
- 未来的研究应该集中在数据收集,方法概括和现实世界的验证上.
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