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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 anincomplete view of the model's prediction quality and performance. We propose anew metric, Average Mahalanobis Distance (AMD) to tackle this issue. AMD is ametric that quantifies how close the whole generated samples are to the groundtruth. We also introduce the Average Maximum Eigenvalue (AMV) metric thatquantifies the overall spread of the predictions. Our metrics are validatedempirically by showing that the ADE/FDE is not sensitive to distributionshifts, giving a biased sense of accuracy, unlike the AMD/AMV metrics. Weintroduce the usage of Implicit Maximum Likelihood Estimation (IMLE) as areplacement for traditional generative models to train our model,Social-Implicit. IMLE training mechanism aligns with AMD/AMV objective ofpredicting trajectories that are close to the ground truth with a tight spread.Social-Implicit is a memory efficient deep model with only 5.8K parameters thatruns in real time of about 580Hz and achieves competitive results. Interactivedemo of the problem can be seen athttps://www.abduallahmohamed.com/social-implicit-amdamv-adefde-demo . Code isavailable at https://github.com/abduallahmohamed/Social-Implicit .

Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation