# DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States

arXiv: https://arxiv.org/abs/2410.05982 · HTML: https://mlsanity.com/paper/2410.05982/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 80 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Built on by (most cited first, 17 of 17)

- [Trajectory Prediction Meets Large Language Models: A Survey](https://mlsanity.com/paper/2506.03408/) (2025)
- [Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics](https://mlsanity.com/paper/2507.12083/) (2025)
- [Autoregressive Meta-Actions for Unified Controllable Trajectory Generation](https://mlsanity.com/paper/2505.23612/) (2025)
- [Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling](https://mlsanity.com/paper/2603.01864/) (2026)
- [Not All Agents Matter: From Global Attention Dilution to Risk-Prioritized Game Planning](https://mlsanity.com/paper/2604.05449/) (2026)
- [A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios](https://mlsanity.com/paper/2510.02627/) (2025)
- [FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier State Space Integration](https://mlsanity.com/paper/2603.01284/) (2026)
- [SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting](https://mlsanity.com/paper/2603.28091/) (2026)
- [Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank](https://mlsanity.com/paper/2605.01393/) (2026)
- [Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility](https://mlsanity.com/paper/2606.15251/) (2026)
- [Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos](https://mlsanity.com/paper/2606.18824/) (2026)
- [Reasonable Motion: A General ASP Foundation for Environment Constrained Movement Trajectory Computation](https://mlsanity.com/paper/2606.25626/) (2026)
- [Towards Metric-Agnostic Trajectory Forecasting](https://mlsanity.com/paper/2607.01133/) (2026)
- [Hybrid Probabilistic Zonotopes for Identifiable and Refinable Predictive Uncertainty](https://mlsanity.com/paper/2608.05454/) (2026)
- [GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction](https://mlsanity.com/paper/2609.13778/) (2026)
- [MoCAR: Motion-code Coordinate-aware AutoRegression for Continuous Trajectory Forecasting](https://mlsanity.com/paper/2610.06210/) (2026)
- [Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data](https://mlsanity.com/paper/2610.12747/) (2026)

## Abstract (arXiv)

Accurate motion forecasting for traffic agents is crucial for ensuring the safety and efficiency of autonomous driving systems in dynamically changing environments. Mainstream methods adopt a one-query-one-trajectory paradigm, where each query corresponds to a unique trajectory for predicting multi-modal trajectories. While straightforward and effective, the absence of detailed representation of future trajectories may yield suboptimal outcomes, given that the agent states dynamically evolve over time. To address this problem, we introduce DeMo, a framework that decouples multi-modal trajectory queries into two types: mode queries capturing distinct directional intentions and state queries tracking the agent's dynamic states over time. By leveraging this format, we separately optimize the multi-modality and dynamic evolutionary properties of trajectories. Subsequently, the mode and state queries are integrated to obtain a comprehensive and detailed representation of the trajectories. To achieve these operations, we additionally introduce combined Attention and Mamba techniques for global information aggregation and state sequence modeling, leveraging their respective strengths. Extensive experiments on both the Argoverse 2 and nuScenes benchmarks demonstrate that our DeMo achieves state-of-the-art performance in motion forecasting.