FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
Not on the map. Why?
Cited by 54 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 239 papers in the map (data through 7 Oct 2026). Builds on 43 of its 53 references that are in the map. First posted on arXiv 21 Apr 2021.
Builds on
43 of 53 references.
References · 43
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- 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
- 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
31 more
- 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
- 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
- 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 251
- 2017Stochastic Variational Video Predictioncited by 234
- 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
- 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
- 2018Orthographic Feature Transform for Monocular 3D Object Detectioncited by 201
- 2019PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settingscited by 178
- 2019Multiple Futures Predictioncited by 178
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- 2019Cross-view Semantic Segmentation for Sensing Surroundingscited by 173
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- 2020Video Panoptic Segmentationcited by 141
- 2019Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactionscited by 117
- 2019Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Datacited by 104
- 2020MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Mapscited by 100
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- 2018Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Drivingcited by 90
- 2018Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networkscited by 84
- 2020FISHING Net: Future Inference of Semantic Heatmaps In Gridscited by 55
- 2017Predicting Deeper into the Future of Semantic Segmentationcited by 51
- 2020Probabilistic Future Prediction for Video Scene Understandingcited by 45
- 2020BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloudcited by 38
- 2019Monocular Plan View Networks for Autonomous Drivingcited by 37
- 2020MultiXNet: Multiclass Multistage Multimodal Motion Predictioncited by 31
- 2018Generative Adversarial Frontal View to Bird View Synthesiscited by 25
- 2017Predicting Scene Parsing and Motion Dynamics in the Futurecited by 20
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- 2019On the Benefit of Adversarial Training for Monocular Depth Estimationcited by 7
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Citing papers per year: 2021: 4; 2022: 37; 2023: 68; 2024: 70; 2025: 38; 2026: 22.
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