SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction

• URL: http://arxiv.org/abs/2401.07729v2
• Date: Mon, 26 Aug 2024 09:16:57 GMT
• Title: SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction
• Authors: Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki,
• Abstract summary: We present SSL-Interactions that proposes pretext tasks to enhance interaction modeling for trajectory prediction. We introduce four interaction-aware pretext tasks to encapsulate various aspects of agent interactions. We also propose an approach to curate interaction-heavy scenarios from datasets.
• Score: 4.286256266868156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses motion forecasting in multi-agent environments, pivotal for ensuring safety of autonomous vehicles. Traditional as well as recent data-driven marginal trajectory prediction methods struggle to properly learn non-linear agent-to-agent interactions. We present SSL-Interactions that proposes pretext tasks to enhance interaction modeling for trajectory prediction. We introduce four interaction-aware pretext tasks to encapsulate various aspects of agent interactions: range gap prediction, closest distance prediction, direction of movement prediction, and type of interaction prediction. We further propose an approach to curate interaction-heavy scenarios from datasets. This curated data has two advantages: it provides a stronger learning signal to the interaction model, and facilitates generation of pseudo-labels for interaction-centric pretext tasks. We also propose three new metrics specifically designed to evaluate predictions in interactive scenes. Our empirical evaluations indicate SSL-Interactions outperforms state-of-the-art motion forecasting methods quantitatively with up to 8% improvement, and qualitatively, for interaction-heavy scenarios.

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