A Set of Control Points Conditioned Pedestrian Trajectory Prediction


  • Inhwan Bae Gwangju Institute of Science and Technology
  • Hae-Gon Jeon Gwangju Institute of Science and Technology




ROB: Motion and Path Planning, ROB: Behavior Learning & Control, ROB: Multi-Robot Systems, CV: Motion & Tracking


Predicting the trajectories of pedestrians in crowded conditions is an important task for applications like autonomous navigation systems. Previous studies have tackled this problem using two strategies. They (1) infer all future steps recursively, or (2) predict the potential destinations of pedestrians at once and interpolate the intermediate steps to arrive there. However, these strategies often suffer from the accumulated errors of the recursive inference, or restrictive assumptions about social relations in the intermediate path. In this paper, we present a graph convolutional network-based trajectory prediction. Firstly, we propose a control point prediction that divides the future path into three sections and infers the intermediate destinations of pedestrians to reduce the accumulated error. To do this, we construct multi-relational weighted graphs to account for their physical and complex social relations. We then introduce a trajectory refinement step based on a spatio-temporal and multi-relational graph. By considering the social interactions between neighbors, better prediction results are achievable. In experiments, the proposed network achieves state-of-the-art performance on various real-world trajectory prediction benchmarks.




How to Cite

Bae, I., & Jeon, H.-G. (2023). A Set of Control Points Conditioned Pedestrian Trajectory Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6155-6165. https://doi.org/10.1609/aaai.v37i5.25759



AAAI Technical Track on Intelligent Robotics