Bayesian Inference of Fog Visibility from LiDAR Point Clouds and Correlation with Probabilities of Detection

Degraded visual environments have strong im- pacts on the quality of LiDAR data. Experiments in artificial fog conditions show that noise points caused by water par- ticles present various distance distributions which depend on visibility. This article introduces a mathematical framework based on Bayesian inference and Markov Chain Monte-Carlo sampling to infer optical visibility from point clouds. The visibility estimation is cast as a classification problem based on the identification of the distance distributions. Contrary to deep learning methods, our approach is model-based and focuses on the design of a full probabilistic framework, more comprehensible, which is critical for autonomous driving. Ul- timately, the impact of the optical visibility on the probability of detection of standard targets is assessed, which can yield improvements on autonomous vehicles performances in adverse weather conditions.