SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction

• URL: http://arxiv.org/abs/2102.00109v1
• Date: Fri, 29 Jan 2021 23:35:00 GMT
• Title: SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction
• Authors: Jasmine Sekhon, Cody Fleming
• Abstract summary: textbfSCAN encodes the influence of spatially close neighbors using a novel spatial attention mechanism. Our approach can also quantitatively outperform state of the art trajectory prediction methods in terms of accuracy of predicted intent.
• Score: 4.507860128918788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safe navigation of autonomous agents in human centric environments requires the ability to understand and predict motion of neighboring pedestrians. However, predicting pedestrian intent is a complex problem. Pedestrian motion is governed by complex social navigation norms, is dependent on neighbors' trajectories, and is multimodal in nature. In this work, we propose \textbf{SCAN}, a \textbf{S}patial \textbf{C}ontext \textbf{A}ttentive \textbf{N}etwork that can jointly predict socially-acceptable multiple future trajectories for all pedestrians in a scene. SCAN encodes the influence of spatially close neighbors using a novel spatial attention mechanism in a manner that relies on fewer assumptions, is parameter efficient, and is more interpretable compared to state-of-the-art spatial attention approaches. Through experiments on several datasets we demonstrate that our approach can also quantitatively outperform state of the art trajectory prediction methods in terms of accuracy of predicted intent.

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