Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras. Our model predicts future instance segmentation and motion of dynamic agents that can be transformed into non-parametric future trajectories. Our approach combines the perception, sensor fusion and prediction components of a traditional autonomous driving stack by estimating bird's-eye-view prediction directly from surround RGB monocular camera inputs. FIERY learns to model the inherent stochastic nature of the future solely from camera driving data in an end-to-end manner, without relying on HD maps, and predicts multimodal future trajectories. We show that our model outperforms previous prediction baselines on the NuScenes and Lyft datasets. The code and trained models are available at https://github.com/wayveai/fiery.
@article{arxiv.2104.10490,
title = {FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras},
author = {Anthony Hu and Zak Murez and Nikhil Mohan and Sofía Dudas and Jeffrey Hawke and Vijay Badrinarayanan and Roberto Cipolla and Alex Kendall},
journal= {arXiv preprint arXiv:2104.10490},
year = {2021}
}