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
Parents
119
317
7
h-index
5
h-index
Korea Advanced Institute of Science and Technology, South Korea
3 publications · 183 citations
3
h-index
3
h-index
Korea Advanced Institute of Science and Technology, South Korea
3 publications · 183 citations
3
h-index
1–5 of 317
124
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
18 publications
2.5K citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
6 h-index
17 publications
959 citations

Delft University of Technology
Delft, The Netherlands
4 h-index
10 publications
880 citations
University of California, Berkeley
Berkeley, United States
4 h-index
11 publications
823 citations
Wuhan University of Technology
Wuhan, China
3 h-index
14 publications
716 citations
Texas A&M University
College Station, United States
3 h-index
6 publications
74 citations
University of New Mexico
Albuquerque, United States
3 h-index
11 publications
187 citations

Ames Research Center
Mountain View, United States
3 h-index
3 publications
60 citations
1–8 of 124
9 publications · 820 citations
9
h-index
4 publications · 305 citations
4
h-index
4 publications · 244 citations
4
h-index
3 publications · 51 citations
3
h-index
Repository · 3 publications · 72 citations
3
h-index
1–5