Geometry-informed Bayesian deep learning for proactive driving risk assessment: a full spatiotemporal probabilistic risk field approach.
- DOI
- 10.1016/j.aap.2026.108783
- Published
- 2026-09-16
- Container
- Accid Anal Prev
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.aap.2026.108783,
title = {Geometry-informed Bayesian deep learning for proactive driving risk assessment: a full spatiotemporal probabilistic risk field approach.},
author = {Hu Z and Zhu F and Ming X and Meng X.},
year = {2026},
journal = {Accid Anal Prev},
doi = {10.1016/j.aap.2026.108783},
url = {https://doi.org/10.1016/j.aap.2026.108783}
}RIS
TY - JOUR TI - Geometry-informed Bayesian deep learning for proactive driving risk assessment: a full spatiotemporal probabilistic risk field approach. AU - Hu Z AU - Zhu F AU - Ming X AU - Meng X. PY - 2026 JO - Accid Anal Prev DO - 10.1016/j.aap.2026.108783 UR - https://doi.org/10.1016/j.aap.2026.108783 ER -
APA
Z, H., F, Z., X, M., & X., M. (2026). Geometry-informed Bayesian deep learning for proactive driving risk assessment: a full spatiotemporal probabilistic risk field approach.. Accid Anal Prev. https://doi.org/10.1016/j.aap.2026.108783
Source records
- europe-pmc · retrieved 2026-09-25T10:28:35.032Z