FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras (2104.10490v3)

Abstract: 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.

• The paper introduces a novel method that predicts future instances in bird’s-eye view using inputs exclusively from monocular cameras.
• It employs an end-to-end, probabilistic learning approach with 3D convolutional temporal processing to effectively forecast dynamic road agents.
• FIERY consistently outperforms LiDAR-based benchmarks on datasets like NuScenes and Lyft, highlighting its potential for improved autonomous driving safety.

Overview of FIERY: Future Instance Prediction in Bird's-Eye View

The paper "FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras" presents a predictive model designed to address challenges in autonomous driving by forecasting the future behavior of dynamic road agents using monocular camera inputs. This approach is motivated by the need for accurate trajectory prediction to enhance safety and decision-making in self-driving vehicles and moves beyond traditional LiDAR-based methods, aiming for a more streamlined and cost-effective camera-based system.

Key Contributions

FIERY's primary contributions are:

1. Bird's-Eye View Prediction: It represents the first model to predict future states in a top-down bird's-eye view using inputs solely from monocular cameras. This perspective is advantageous for planning and decision-making in autonomous systems.
2. Probabilistic Modeling: The model captures the inherent uncertainty and variability in predicting future dynamics, providing a multimodal view of possible outcomes.
3. Performance Improvements: The model consistently surpasses existing baselines for prediction tasks on widely used datasets, such as NuScenes and Lyft, highlighting its efficacy and accuracy.

Technical Approach

FIERY is built on several technical innovations:

• 3D Representation from Monocular Cameras: The model lifts 2D camera features into a 3D representation, projecting them into a bird's-eye view. It uses depth distributions predicted from image embeddings to manage the transformation uncertainties effectively.
• End-to-End Learning: The system integrates perception with sensor fusion and prediction tasks within a unified framework, avoiding the segmented pipeline common in traditional systems.
• Temporal and Spatial Processing: Incorporating a 3D convolutional temporal model, FIERY effectively utilizes past observations, which are transformed into the current frame of reference using a spatial transformer.
• Stochastic Future Prediction: Through a variational approach, the model generates a range of potential future scenarios by sampling from learned probabilistic distributions.

Results and Implications

The evaluation reveals that FIERY not only exceeds performance benchmarks in bird's-eye view segmentation but also outperforms models based on LiDAR inputs, supporting the potential of camera-based systems to replace costlier sensors. The model's ability to predict temporally consistent future instances is a significant leap towards deploying robust autonomous systems capable of handling real-world scenarios.

Future Prospects and Implications

The promising results suggest several directions for further research and development:

• Extended Temporal Predictions: Enhancing the temporal prediction horizon could offer greater foresight and enable better planning in complex traffic scenarios.
• Integration with Control Systems: FIERY's predictive capabilities can be integrated with autonomous driving policies, further enhancing navigation strategies and real-time decision-making.
• Multi-Modal Systems: Combining FIERY with other sensory modalities (e.g., radar) could provide even richer data for autonomous systems, potentially increasing robustness and reliability.

In conclusion, FIERY represents a significant advancement in future prediction models for autonomous driving, offering a practical, efficient, and high-performance solution using monocular camera inputs. This work lays a foundation for further explorations in multi-agent dynamics and probabilistic models in the domain of autonomous navigation.