  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 directly 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. Code is available at https://github.com/wayveai/fiery
