A two-stage joint trajectory prediction model that uses trajectory-type classification in a marginal proposal stage and keypoint-guided joint refinement beats prior methods on Waymo interaction metrics for position error.
$AIR^2$ for Interaction Prediction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
The 2021 Waymo Interaction Prediction Challenge introduced a problem of predicting the future trajectories and confidences of two interacting agents jointly. We developed a solution that takes an anchored marginal motion prediction model with rasterization and augments it to model agent interaction. We do this by predicting the joint confidences using a rasterized image that highlights the ego agent and the interacting agent. Our solution operates on the cartesian product space of the anchors; hence the $"^2"$ in $AIR^2$. Our model achieved the highest mAP (the primary metric) on the leaderboard.
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
JAM: Keypoint-Guided Joint Prediction after Classification-Aware Marginal Proposal for Multi-Agent Interaction
A two-stage joint trajectory prediction model that uses trajectory-type classification in a marginal proposal stage and keypoint-guided joint refinement beats prior methods on Waymo interaction metrics for position error.