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A physics-informed uncertainty quantification framework for deep learning-based real-time lane-change intention prediction

Zhuo Cao et al.

Transportation Research Part C: Emerging Technologies2026https://doi.org/10.1016/j.trc.2026.105637article
ABDC A*
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0.50

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https://doi.org/https://doi.org/10.1016/j.trc.2026.105637

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@article{zhuo2026,
  title        = {{A physics-informed uncertainty quantification framework for deep learning-based real-time lane-change intention prediction}},
  author       = {Zhuo Cao et al.},
  journal      = {Transportation Research Part C: Emerging Technologies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.trc.2026.105637},
}

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A physics-informed uncertainty quantification framework for deep learning-based real-time lane-change intention prediction

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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