Performance Modeling a Near-Infrared ToF LiDAR Under Fog: A Data-Driven Approach.

As a critical sensor for high-level autonomous vehicles, LiDAR’s limitations in adverse weather (e.g. rain, fog, snow, etc.) impede the deployment of self-driving cars in all weather conditions. However, studies in literature on LiDAR’s performance in harsh conditions are insufficient. In this paper, based on a dataset collected with a popular Near-InfraRed (NIR) ToF LiDAR in a well-controlled artificial fog chamber, we statistically model the LiDAR ranging process in fog conditions through a data-driven approach. Specifically, giving an object at a known distance, our model is able to predict LiDAR measures (range and intensity) under various fog conditions. For a transmitted laser under fog, we first model and predict the minimum visibility required to detect its true range or not. Then, the noisy range and intensity measures are sampled from the probabilistic measurement distributions inferred from the dataset. The performance of the proposed method has been quantitatively and qualitatively evaluated. Experimental results show that our approach can provide a promising performance prediction of the utilized NIR ToF LiDAR under fog, which opens a new gate to the quantitative assessment of adverse weather and contributes to the specification of relevant Operational Domain Designs (ODDs). The developed ROS package is available at: https://github.com/cavayangtao/lanoising. [ABSTRACT FROM AUTHOR]