LiDAR-based Dynamic Blockage Prediction: A Data-driven Approach for Learning Interactive Bayesian Models

TL;DR

This paper introduces a data-driven method using interactive Bayesian models and particle filtering to predict LiDAR sensor blockages in autonomous vehicles, enhancing safety and interpretability.

Contribution

It develops a novel interactive generalized dynamic Bayesian network and an inference algorithm for accurate blockage prediction and abnormality detection in vehicular sensing.

Findings

Effective prediction of LiDAR blockages demonstrated

Enhanced explainability and adaptability in blockage scenarios

Improved detection of sensor abnormalities

Abstract

Vehicular sensing-based intelligence has made substantial progress in transportation systems, leading to higher levels of safety and sustainability for smart cities and autonomous systems. This paper proposes a new approach to learn an interactive generalized dynamic Bayesian network (I-GDBN) model aiming to predict future LiDAR sensor blockages from time-sequence-based 3D point cloud perception. During learning, separate GDBN models are trained for various vehicles in normal and blockage situations. To perform the interaction between multiple vehicles, a high-level vocabulary is formed. Initially, during testing, the best generative model for either normal or blockage situations is selected. An interactive Markov jump particle filter (I-MJPF) is then proposed to leverage the probabilistic information provided by the I-GDBN to infer the blockages and detect the abnormalities at the high abstraction level. The proposed interactive model allows better self-aware and explainable capabilities that can adapt to blockage scenarios, which is also helpful when sensors fail to provide observations.