Probabilistic Rainfall Estimation from Automotive Lidar

• URL: http://arxiv.org/abs/2104.11467v1
• Date: Fri, 23 Apr 2021 08:35:54 GMT
• Title: Probabilistic Rainfall Estimation from Automotive Lidar
• Authors: Robin Karlsson, David Robert Wong, Kazunari Kawabata, Simon Thompson, Naoki Sakai
• Abstract summary: This work presents a probabilistic hierarchical Bayesian model that infers rainfall rate from automotive lidar point cloud sequences. The results show prediction accuracy comparable to the measurement resolution of a disdrometer, and the soundness and usefulness of the uncertainty estimation.
• Score: 1.0499611180329804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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