关于可驾驶面积估计的最新发展:一项调查和功能分析
Juan Luis Hortelano1, Jorge Villagrá1, Jorge Godoy1
1Centro de Automática y Robótica, CSIC-Universidad Politécnica de Madrid, Ctra. Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本综述探讨了自动驾驶系统 (ADS) 的在线可驾驶面积估计. 它为分析绘图技术提供了一个新的框架,对于缺乏高清地图的地区至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 目前的自动驾驶系统 (ADS) 依赖于昂贵的,经常更新的高清地图.
- 这种依赖限制了ADS的运营设计领域 (ODD).
- 在线地图创建为传统的高清地图提供了经济高效的替代方案.
研究的目的:
- 为ADS提供可驾驶面积估计技术的最新审查.
- 引入一种新的架构分解,用于分析基于学习和非基于学习的算法.
- 为在HD地图可用性有限或不存在的环境中部署ADS提供实用见解.
主要方法:
- 对最近和有影响力的可驾驶面积估计算法进行全面审查.
- 提出了一个用于分类和分析各种算法方法的新型架构.
- 分析现代传感技术对可驾驶面积估计的影响.
主要成果:
- 该审查对一系列可驾驶面积估计技术进行了分类和分析.
- 提出了一个框架来比较基于学习和非基于学习的方法.
- 确定了用于评估和比较可驾驶面积估计算法的关键数据集.
结论:
- 驾驶区域估计对于扩大ADS运营能力超出高清地图覆盖范围至关重要.
- 拟议的架构分解有助于更深入地了解现有和未来的算法.
- 该审查为自动驾驶领域的研究人员和从业人员提供了宝贵的资源.
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