Map-aware Trajectory Prediction

Map-Aware Trajectory Prediction is a trajectory prediction framework that incorporates spatial and semantic information from high-definition maps to constrain and inform motion forecasts, distinguishing itself from generic trajectory prediction methods by explicitly leveraging lane topology, road constraints, and environmental context to improve prediction accuracy and plausibility.

17

Publications

2.2K

Citations

66

Authors

23

Institutions

Publications per year

2017–2026

11

Authors

66

  1. DR
    Deva Ramanan

    Carnegie Mellon University, United States

    2 publications · 1.6K citations

    2

    h-index

  2. MM
    Mohan M. Trivedi

    University of California San Diego, United States

    2 publications · 94 citations

    2

    h-index

  3. JS
    Jagjeet Singh

    Carnegie Mellon University, United States

    2 publications · 1.6K citations

    2

    h-index

  4. WD
    Wang De

    Georgia Institute of Technology, United States

    2 publications · 1.6K citations

    2

    h-index

  5. JL
    John Lambert

    Georgia Institute of Technology, United States

    2 publications · 1.6K citations

    2

    h-index

1–5 of 66

Venues

  1. IEEE Robotics and Automation Letters

    2 publications · 52 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 168 citations

    2

    h-index

  3. 1

    h-index

  4. Sensors

    1 publications · 31 citations

    1

    h-index

  5. KTH Publication Database DiVA (KTH Royal Institute of Technology)

    Repository · 1 publications · 40 citations

    1

    h-index

1–5