Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted in-depth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14 % for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.

本文阐述了在恶劣天气条件下，特别是大雨和浓雾下，汽车激光雷达传感器的性能表现和探测方法。通过数据分析，提出了一种新的只基于激光雷达探测雨雾的方法，并取得了显著的效果。研究表明，这些探测技术可以提高自动驾驶在恶劣天气条件下的安全性。

基于汽车激光雷达传感器的天气影响与分类