SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction
DOI:
https://doi.org/10.1609/aaai.v35i7.16762Keywords:
Motion and Path Planning, Multiagent PlanningAbstract
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 SCAN, a Spatial Context Attentive Network 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.Downloads
Published
2021-05-18
How to Cite
Sekhon, J. ., & Fleming, C. (2021). SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 35(7), 6119-6127. https://doi.org/10.1609/aaai.v35i7.16762
Issue
Section
AAAI Technical Track on Intelligent Robots