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We introduce a novel trajectory predictor that considers social interactions among agents, maintaining spatial-temporal information over an extended temporal horizon. It achieves high accuracy, generalisability, and outperforms recent SOTA algorithms on NGSIM and HIGHD Datasets
Code for: "Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation" Accepted @ ECCV2022
[ECCV 2022 Oral] Official Implementation of "Social-SSL: Self-Supervised Cross-Sequence Representation Learning Based on Transformers for Multi-Agent Trajectory Prediction"