利用基于LiDAR的模拟来量化农村环境中自动驾驶汽车静态环境的复杂性
Mohamed Abohassan1, Karim El-Basyouny1
1Department of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
自动驾驶汽车 (AV) 由于路边特征而面临越来越多的环境复杂性,影响处理需求. 暴雨显著提高了AV处理需求,突出了安全导航的基础设施需求.
科学领域:
- 环境科学 环境科学
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
背景情况:
- 自动驾驶汽车 (AV) 需要复杂的环境感知,以确保安全的导航.
- 了解环境复杂性对于AV系统设计和性能评估至关重要.
研究的目的:
- 量化环境因素对自动驾驶汽车遇到的场景复杂性的影响.
- 开发一个框架来估计AVS的实时数据处理需求.
主要方法:
- 使用虚拟模拟 (VISTA模拟器) 来建模车辆与环境的相互作用.
- 将道路切割成相关的道路特征 (RRF) 和完整的环境 (FE),以分析场景的复杂性.
- 基于路边特征,道路几何形状和天气条件等环境变量,量化处理要求.
主要成果:
- 路边设施增加了高达400%的环境复杂性.
- 添加一条车道增加了12.3-16.5%的处理要求;峰曲线导致4.2%的数据丢失,而斜曲线增加了7%的复杂性.
- 与正常条件相比,大雨使AV处理需求增加了240%.
结论:
- 环境复杂性对AV处理需求产生重大影响,路边特征和恶劣天气是主要因素.
- 调查结果为AV开发人员提供了关键数据,以优化设计,并为机构规划基础设施.
- 解决环境复杂性对于安全有效地部署自动驾驶汽车至关重要.
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