LiDAR-based Dynamic Blockage Prediction: A Data-driven Approach for Learning Interactive Bayesian Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909004946472960 |
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| author | Memon, Saleemullah Krayani, Ali Zontone, Pamela Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo |
| author_facet | Memon, Saleemullah Krayani, Ali Zontone, Pamela Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_28040 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LiDAR-based Dynamic Blockage Prediction: A Data-driven Approach for Learning Interactive Bayesian Models Memon, Saleemullah Krayani, Ali Zontone, Pamela Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo Signal Processing 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. |
| title | LiDAR-based Dynamic Blockage Prediction: A Data-driven Approach for Learning Interactive Bayesian Models |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.28040 |