Cross-modal Prediction

Cross-Modal Prediction is a predictive modeling framework that integrates and aligns information from disparate sensory modalities—such as visual, LiDAR, and radar data—to forecast the future states of traffic participants by leveraging the complementary strengths of each modality within a unified spatiotemporal representation.

57

Publications

3.3K

Citations

220

Authors

104

Institutions

Publications per year

2017–2026

40

Authors

220

  1. LT
    Lorenzo Torresani

    Dartmouth College, United States

    2 publications · 308 citations

    2

    h-index

  2. BK
    Bruno Korbar

    Dartmouth College, United States

    2 publications · 308 citations

    2

    h-index

  3. AT
    Antonio Torralba

    Massachusetts Institute of Technology, United States

    2 publications · 137 citations

    2

    h-index

  4. RT
    Russ Tedrake

    Massachusetts Institute of Technology, United States

    2 publications · 137 citations

    2

    h-index

  5. JZ
    Jun-Yan Zhu

    Carnegie Mellon University, United States

    2 publications · 137 citations

    2

    h-index

1–5 of 220

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 290 citations

    3

    h-index

  2. Pattern Recognition

    2 publications · 87 citations

    2

    h-index

  3. Cerebral Cortex

    2 publications · 205 citations

    2

    h-index

  4. Journal of Behavioral Decision Making

    1 publications · 532 citations

    1

    h-index

  5. 1

    h-index

1–5