在移动的表面上建模天气降雨强度,适用于自动驾驶汽车
Mateus Carvalho1, Horia Hangan1
1Department of Mechanical Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究提出了一个模型来量化在恶劣天气下自动驾驶汽车 (AV) 的降水量. 该模型通过分析雨雪如何影响自动驾驶汽车来帮助优化传感器的放置,从而提高安全性和性能.
科学领域:
- 自主系统 自主系统
- 环境科学 环境科学
- 机械工程 机械工程
背景情况:
- 自动驾驶汽车 (AV) 的性能在恶劣天气下下降,由于可见性降低和降水积累,影响传感器功能.
- 了解降水效应对于在各种环境条件下可靠的AV运行至关重要.
研究的目的:
- 开发和验证一个模型来量化降雨 (雨和雪),通过移动的自动驾驶汽车来感知.
- 分析天气参数 (如风和粒子大小) 对降水强度的影响.
- 确定最佳的传感器定位,以最大限度地减少AV表面的降水流.
主要方法:
- 开发了一种包含风向,粒子大小分布和表面倾斜的数学模型.
- 在各种倾斜的表面和沿着圆形的驾驶路径计算降水强度.
- 部分验证的模型输出与测试车辆的直接测量相比.
主要成果:
- 该模型表明其输出与雨水和雪的实验数据之间存在强烈的相关性.
- 结果强调了探测表面角度对感知降水水平的关键影响.
- 确定了最佳的表面方向,以尽量减少降水积累.
结论:
- 拟议的分析模型准确量化了降水对AVS的影响.
- 这些发现对于制定大雨减缓策略和设计强大的传感器系统至关重要.
- 该模型有助于确定最佳的传感器定位,以提高在恶劣天气中的AV性能.
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