Goal-oriented Prediction

Goal-Oriented Prediction is a methodological framework within traffic participant prediction that identifies and models the intended future states or destinations of agents by integrating high-level intent inference with trajectory forecasting, distinguishing itself from purely kinematic or reactive approaches through its focus on goal-driven behavioral reasoning.

48

Publications

2.7K

Citations

147

Authors

81

Institutions

Publications per year

2017–2026

26

Authors

147

  1. KK
    Kris Kitani

    Carnegie Mellon University, United States

    3 publications · 208 citations

    3

    h-index

  2. NR
    Nicholas Rhinehart

    University of California, Berkeley, United States

    3 publications · 208 citations

    3

    h-index

  3. JG
    Junru Gu

    Tsinghua University, China

    3 publications · 485 citations

    3

    h-index

  4. SL
    Sergey Levine

    University of California, Berkeley, United States

    3 publications · 86 citations

    3

    h-index

  5. AL
    Amanda L. Woodward

    University of Chicago, United States

    3 publications · 261 citations

    3

    h-index

1–5 of 147

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 89 citations

    5

    h-index

  2. Developmental Science

    2 publications · 231 citations

    2

    h-index

  3. 2

    h-index

  4. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 2 publications · 528 citations

    2

    h-index

  5. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 1 publications · 21 citations

    1

    h-index

1–5