Probabilistic Collision Avoidance

Probabilistic Collision Avoidance is a collision avoidance control framework that belongs to the class of probabilistic safety-critical decision-making systems, distinguished by its incorporation of uncertainty quantification and stochastic models to compute and ensure bounded collision risk probabilities in dynamic environments.

119

Publications

4.9K

Citations

317

Authors

124

Institutions

Publications per year

2017–2026

48

Authors

317

  1. MJ
    Mykel J. Kochenderfer

    Stanford University, United States

    7 publications · 442 citations

    7

    h-index

  2. JC
    James Chryssanthacopoulos

    Columbia University, United States

    5 publications · 255 citations

    5

    h-index

  3. YC
    Yonghoon Cho

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 183 citations

    3

    h-index

  4. JA
    Javier Alonso–Mora

    Delft University of Technology, Netherlands

    3 publications · 385 citations

    3

    h-index

  5. JK
    Jinwhan Kim

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 183 citations

    3

    h-index

1–5 of 317

Venues

  1. 9

    h-index

  2. Journal of Guidance Control and Dynamics

    4 publications · 305 citations

    4

    h-index

  3. 4

    h-index

  4. Acta Astronautica

    3 publications · 51 citations

    3

    h-index

  5. arXiv (Cornell University)

    Repository · 3 publications · 72 citations

    3

    h-index

1–5