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arXiv · 2604.28040

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

Saleemullah Memon·Ali Krayani·Pamela Zontone·Lucio Marcenaro·David Martin Gomez·Carlo Regazzoni
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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.

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Saleemullah Memon, Ali Krayani, Pamela Zontone, Lucio Marcenaro, David Martin Gomez, Carlo Regazzoni. 2026-04-30. LiDAR-based Dynamic Blockage Prediction: A Data-driven Approach for Learning Interactive Bayesian Models. https://arxiv.org/abs/2604.28040

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