SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction
- URL: http://arxiv.org/abs/2401.07729v1
- Date: Mon, 15 Jan 2024 14:43:40 GMT
- Title: SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction
- Authors: Prarthana Bhattacharyya, Chengjie Huang and 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: 4.817322497343503
- 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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