What Happens for a ToF LiDAR in Fog?

TLDR

LiDAR uses laser transmission and return processing to generate high‑accuracy 3D point clouds essential for autonomous driving, yet its ranging performance degrades in adverse weather such as fog, limiting all‑weather autonomy. This article investigates how a typical time‑of‑flight LiDAR performs in foggy conditions. By varying fog density in the CEREMA Adverse Weather Facility, the study qualitatively and quantitatively examined ranging performance and trained a machine‑learning model to predict the minimum fog visibility enabling successful ranging. The experimental results and predictive model provide guidance for automotive industry specifications of ToF LiDARs.

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.