@Inproceedings{ChristianosKarkusEtAl2023,
  author = {Christianos, F. and Karkus, P. and Ivanovic, B. and Albrecht, S. V. and Pavone, M.},
  title = {Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models},
  abstract = {Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predicting occluded agents based on the behaviour of nearby visible agents; however, as we show in experiments, these models are difficult to integrate into downstream planning. To this end, we propose Bi-level Variational Occlusion Models (BiVO), a two-step generative model that first predicts likely locations of occluded agents, and then generates likely trajectories for the occluded agents. In contrast to existing methods, BiVO outputs a trajectory distribution which can then be sampled from and integrated into standard downstream planning. We evaluate the method in closed-loop replay simulation using the real-world nuScenes dataset. Our results suggest that BiVO can successfully learn to predict occluded agent trajectories, and these predictions lead to better subsequent motion plans in critical scenarios.},
  month = may,
  year = {2023},
  address = {London, UK},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  owner = {borisi},
  timestamp = {2023-01-18},
  url = {https://arxiv.org/abs/2210.14584}
}