FutureDet with Refinement Stage

In this blog post, we present the idea to enhance FutureDet by incorporating a two-stage motion prediction framework, where the first stage generates coarse future motion predictions, and a refinement stage introduces fine-grained adjustments using interaction and map-based context. Below, we outline the key advantages of FutureDet’s backcasting mechanism and its current limitations, followed by a detailed argument for why a two-stage refinement approach is a natural and effective way to address these gaps.

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1. Recap of FutureDet’s Strengths and Limitations

FutureDet (paper, code) excels at jointly detecting objects in the present and near-future frames and then stitching them into multi-step trajectories via its backcasting mechanism. This design confers several advantages:

1. Robust Base Detector (CenterPoint):
   FutureDet inherits CenterPoint’s robust detection capabilities, producing high-quality, Gaussian-modeled heatmaps in BEV space.

2. Multi-Futures (Multi-Modality):
   By treating each future frame independently, FutureDet can produce multiple plausible future trajectories, effectively capturing multi-modality in agent behavior.

3. Flexible Trajectory Matching (Backcasting):
   The matching procedure between frames (via backcasting) allows multiple potential futures to map to a single current detection. This is crucial for handling uncertainty in real-world scenarios.

Despite these strengths, several major gaps remain:

1. Lack of Interaction Modeling:
   FutureDet treats each agent’s future independently, which ignores the fact that agents’ behaviors affect each other (e.g., vehicles yield to pedestrians, or cars follow one another on a highway).

2. Lack of Scene/Map Context:
   FutureDet does not incorporate High-Definition (HD) maps that encode lanes, road boundaries, traffic lights, etc. These constraints strongly shape how agents move, particularly at complex intersections.

3. Inter-Class Interactions:
   FutureDet’s design does not directly model how different classes (e.g., car vs. pedestrian) may interact (e.g., a pedestrian crossing might force a car to stop).

4. No Explicit Multi-Trajectory Diversity Enforcement:
   While multi-modalities emerge from the independent frame detections, there is no explicit mechanism to ensure that these multiple futures are sufficiently diverse or comply with map constraints.

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2. Motivation for a Two-Stage (Refinement) Approach

2.1 Overcoming the Complexity of One-Stage Interaction Modeling

Attempting to directly infuse interaction and map modeling into FutureDet’s single forward pass can be overly complex, because:

• Interaction Complexity:
  Modeling interactions in a fully end-to-end manner (at detection time) significantly complicates the architecture. FutureDet currently predicts all future frames in parallel, and retroactively stitches them via backcasting. Adding interaction constraints would require the model to reason about the entire set of agent trajectories (including multi-futures) all at once, which poses combinatorial challenges.

• Map Constraints:
  Integrating map context in a single stage would require the network to learn both detection/forecasting tasks and dense map-constraint reasoning simultaneously. This can dilute the network’s capacity to focus on accurate detection and coarse motion estimation.

2.2 Benefits of a Separate Refinement Stage

A refinement stage—applied after FutureDet’s coarse predictions are generated—can focus solely on improving and “correcting” the initial trajectories using more detailed contextual information:

1. Modular Interaction Modeling:
   The refinement network can take as input the coarse FutureDet trajectories for all agents, along with relational/graph-based features that capture which agents are neighbors, their relative positions, and potential collision or cooperation scenarios.

2. Map Modeling Integration:
   The refinement module can explicitly leverage HD maps (e.g., lane geometry, traffic signals) to adjust or prune coarse forecasts that violate map constraints or traffic rules. By focusing only on this refinement step, the complexity of the map modeling process is isolated and more tractable.

3. Coarse-to-Fine Approach:
   A two-stage pipeline allows the initial stage to solve a simpler problem (“Where might the object be over time in a general sense?”). The second stage then solves a more specialized problem (“Which of these coarse trajectories are dynamically feasible given agent interactions and map rules, and how can we fine-tune them?”).

4. Progressive Distribution Alignment:
   If the model’s job in the second stage is to refine predictions, it can more effectively handle distribution shifts or uncertainties by focusing on local adjustments. This progressive learning strategy often yields higher accuracy than tackling coarse and fine tasks simultaneously in a single network.

5. Compatibility with Existing Leaderboard Trends:
   Empirically, top-performing methods on large-scale datasets (nuScenes, Argoverse, WOD) frequently employ multi-stage or refine-based frameworks that allow the model to incorporate more scene constraints after producing an initial set of predictions.

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3. Motion Prediction Approaches with Refinement Stages

Below, we highlight several state-of-the-art approaches that utilize refinement stages to address the challenges of multi-agent and multi-modal motion prediction, providing links to their papers and code for further exploration. These sources provide valuable insights and practical tools to guide the implementation of the refinement stage in FutureDet.

• [NeurIPS 2022] MTR - Motion Transformer with Global Intention Localization and Local Movement Refinement [paper, code]
• [CVPR 2023] QCNet - Query-Centric Trajectory Prediction [paper, code]
• [ICCV 2023] R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement [paper]
• [ICCV 2023] DCMS - Bootstrap Motion Forecasting With Self-Consistent Constraints [paper]
• [TPAMI 2024] MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying [paper, code]
• [CVPR 2024] SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction [paper, code]
• [CVPRW 2024] LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints [paper, code]