  Robust sensing and perception in adverse weather conditions remains one of
the biggest challenges for realizing reliable autonomous vehicle mobility
services. Prior work has established that rainfall rate is a useful measure for
adversity of atmospheric weather conditions. This work presents a probabilistic
hierarchical Bayesian model that infers rainfall rate from automotive lidar
point cloud sequences with high accuracy and reliability. The model is a
hierarchical mixture of expert model, or a probabilistic decision tree, with
gating and expert nodes consisting of variational logistic and linear
regression models. Experimental data used to train and evaluate the model is
collected in a large-scale rainfall experiment facility from both stationary
and moving vehicle platforms. The results show prediction accuracy comparable
to the measurement resolution of a disdrometer, and the soundness and
usefulness of the uncertainty estimation. The model achieves RMSE 2.42 mm/h
after filtering out uncertain predictions. The error is comparable to the mean
rainfall rate change of 3.5 mm/h between measurements. Model parameter studies
show how predictive performance changes with tree depth, sampling duration, and
crop box dimension. A second experiment demonstrate the predictability of
higher rainfall above 300 mm/h using a different lidar sensor, demonstrating
sensor independence.
