# ControlMTR: Control-Guided Motion Transformer with Scene-Compliant Intention Points for Feasible Motion Prediction

arXiv: https://arxiv.org/abs/2404.10295 · HTML: https://mlsanity.com/paper/2404.10295/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2024.

## Built on by (most cited first, 13 of 13)

- [DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba](https://mlsanity.com/paper/2408.03601/) (2024)
- [DriveGPT: Scaling Autoregressive Behavior Models for Driving](https://mlsanity.com/paper/2412.14415/) (2024)
- [KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation](https://mlsanity.com/paper/2407.12940/) (2024)
- [TrajFlow: Multi-modal Motion Prediction via Flow Matching](https://mlsanity.com/paper/2506.08541/) (2025)
- [Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories](https://mlsanity.com/paper/2506.02571/) (2025)
- [ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties](https://mlsanity.com/paper/2405.00797/) (2024)
- [Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation](https://mlsanity.com/paper/2412.16750/) (2024)
- [Dynamic Intent Queries for Motion Transformer-based Trajectory Prediction](https://mlsanity.com/paper/2504.15766/) (2025)
- [PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Driving](https://mlsanity.com/paper/2509.18609/) (2025)
- [PlanTRansformer: Unified Prediction and Planning with Goal-conditioned Transformer](https://mlsanity.com/paper/2602.03376/) (2026)
- [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)
- [Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank](https://mlsanity.com/paper/2605.01393/) (2026)

## Abstract (arXiv)

The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction method, alleviated mode collapse and instability during training and enhanced overall prediction performance by replacing conventional dense future endpoints with a small set of fixed prior motion intention points. However, the fixed prior intention points make the MTR multi-modal prediction distribution over-scattered and infeasible in many scenarios. In this paper, we propose the ControlMTR framework to tackle the aforementioned issues by generating scene-compliant intention points and additionally predicting driving control commands, which are then converted into trajectories by a simple kinematic model with soft constraints. These control-generated trajectories will guide the directly predicted trajectories by an auxiliary loss function. Together with our proposed scene-compliant intention points, they can effectively restrict the prediction distribution within the road boundaries and suppress infeasible off-road predictions while enhancing prediction performance. Remarkably, without resorting to additional model ensemble techniques, our method surpasses the baseline MTR model across all performance metrics, achieving notable improvements of 5.22% in SoftmAP and a 4.15% reduction in MissRate. Our approach notably results in a 41.85% reduction in the cross-boundary rate of the MTR, effectively ensuring that the prediction distribution is confined within the drivable area.