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

Authors

  • Jasmine Sekhon University of Virginia
  • Cody Fleming University of Virginia Iowa State University

Keywords:

Motion and Path Planning, Multiagent Planning

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 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.

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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. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16762

Issue

Section

AAAI Technical Track on Intelligent Robots