Multi-agent Trajectory Prediction

Multi-Agent Trajectory Prediction is a computational task in which the future spatial paths of multiple interacting agents are jointly predicted, distinguished from single-agent approaches by explicitly modeling interdependencies among agents within a shared environment.

118

Publications

7.8K

Citations

413

Authors

133

Institutions

Publications per year

2017–2026

114

Authors

413

  1. MT
    Masayoshi Tomizuka

    University of California, Berkeley, United States

    10 publications · 447 citations

    10

    h-index

  2. JL
    Jiachen Li

    University of California, Berkeley, United States

    8 publications · 396 citations

    8

    h-index

  3. HM
    Hengbo Ma

    University of California, Berkeley, United States

    6 publications · 280 citations

    6

    h-index

  4. CC
    Chiho Choi

    Honda (United States), United States

    5 publications · 227 citations

    5

    h-index

  5. KK
    Kris Kitani

    Carnegie Mellon University, United States

    4 publications · 699 citations

    4

    h-index

1–5 of 413

Venues

  1. arXiv (Cornell University)

    Repository · 20 publications · 873 citations

    14

    h-index

  2. 11

    h-index

  3. IEEE Robotics and Automation Letters

    5 publications · 269 citations

    6

    h-index

  4. IEEE Transactions on Vehicular Technology

    3 publications · 503 citations

    4

    h-index

  5. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 4 publications · 1.1K citations

    4

    h-index

1–5