# RMP-YOLO: A Robust Motion Predictor for Partially Observable Scenarios even if You Only Look Once

arXiv: https://arxiv.org/abs/2409.11696 · HTML: https://mlsanity.com/paper/2409.11696/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [ModeSeq: Taming Sparse Multimodal Motion Prediction with Sequential Mode Modeling](https://mlsanity.com/paper/2411.11911/) (2024)
- [AGI-Elo: How Far Are We From Mastering A Task?](https://mlsanity.com/paper/2505.12844/) (2025)
- [HeLoFusion: An Efficient and Scalable Encoder for Modeling Heterogeneous and Multi-Scale Interactions in Trajectory Prediction](https://mlsanity.com/paper/2509.11719/) (2025)
- [FlowS: One-Step Motion Prediction via Local Transport Conditioning](https://mlsanity.com/paper/2604.26065/) (2026)
- [Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling](https://mlsanity.com/paper/2605.24037/) (2026)
- [Towards Metric-Agnostic Trajectory Forecasting](https://mlsanity.com/paper/2607.01133/) (2026)

## Abstract (arXiv)

We introduce RMP-YOLO, a unified framework designed to provide robust motion predictions even with incomplete input data. Our key insight stems from the observation that complete and reliable historical trajectory data plays a pivotal role in ensuring accurate motion prediction. Therefore, we propose a new paradigm that prioritizes the reconstruction of intact historical trajectories before feeding them into the prediction modules. Our approach introduces a novel scene tokenization module to enhance the extraction and fusion of spatial and temporal features. Following this, our proposed recovery module reconstructs agents' incomplete historical trajectories by leveraging local map topology and interactions with nearby agents. The reconstructed, clean historical data is then integrated into the downstream prediction modules. Our framework is able to effectively handle missing data of varying lengths and remains robust against observation noise, while maintaining high prediction accuracy. Furthermore, our recovery module is compatible with existing prediction models, ensuring seamless integration. Extensive experiments validate the effectiveness of our approach, and deployment in real-world autonomous vehicles confirms its practical utility. In the 2024 Waymo Motion Prediction Competition, our method, RMP-YOLO, achieves state-of-the-art performance, securing third place.