FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
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.
PDF Abstract ICCV 2021 PDF ICCV 2021 AbstractCode
Colab
Datasets
nuScenes
Results from the Paper
Ranked #1 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU veh - 224x480 - No vis filter - 100x50 at 0.25 metric)
