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A dynamic prompting and scenario generation method for autonomous driving perception via large-model optimization

Song Zhang et al.

Transportation Research Part C: Emerging Technologies2026https://doi.org/10.1016/j.trc.2026.105672article
ABDC A*
Weight
0.50

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https://doi.org/https://doi.org/10.1016/j.trc.2026.105672

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@article{song2026,
  title        = {{A dynamic prompting and scenario generation method for autonomous driving perception via large-model optimization}},
  author       = {Song Zhang et al.},
  journal      = {Transportation Research Part C: Emerging Technologies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.trc.2026.105672},
}

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A dynamic prompting and scenario generation method for autonomous driving perception via large-model optimization

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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

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.