Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation. (arXiv:2203.03057v2 [cs.CV] UPDATED)

Best-of-N (BoN) Average Displacement Error (ADE)/ Final Displacement Error
(FDE) is the most used metric for evaluating trajectory prediction models. Yet,
the BoN does not quantify the whole generated samples, resulting in an
incomplete view of the model's prediction quality and performance. We propose a
new metric, Average Mahalanobis Distance (AMD) to tackle this issue. AMD is a
metric that quantifies how close the whole generated samples are to the ground
truth. We also introduce the Average Maximum Eigenvalue (AMV) …

arxiv evaluation likelihood maximum likelihood estimation prediction social