Abstract
By transmitting lasers and processing laser returns, LiDAR (light detection and ranging) perceives the surrounding environment through distance measurements. Because of high ranging accuracy, LiDAR is one of the most critical sensors in autonomous driving systems. Revolving around the 3D point clouds generated from LiDARs, plentiful algorithms have been developed for object detection/tracking, environmental mapping, or localization. However, a LiDAR’s ranging performance suffers under adverse weather (e.g. fog, rain, snow etc.), which impedes full autonomous driving in all weather conditions. This article focuses on analyzing the performance of a typical time-of-flight (ToF) LiDAR under fog environment. By controlling the fog density within CEREMA Adverse Weather Facility, the relations between the ranging performance and fogs are both qualitatively and quantitatively investigated. Furthermore, based on the collected data, a machine learning based model is trained to predict the minimum fog visibility that allows successful ranging for this type of LiDAR. The revealed experimental results and methods are helpful for ToF LiDAR specifications from automotive industry.
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