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


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



Motion and Path Planning, Multiagent Planning


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.




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.



AAAI Technical Track on Intelligent Robots